Special Edition: Guest Blog by Assistant Professor of Practice (Urban Forestry), Jennifer Killian
When I was asked to create a new course for Oregon State University’s Ecampus program, my first reaction was a mix of sheer excitement… and, well, a little terror. I’ve built workshops, presentations, and even all-day trainings, but assembling ten weeks of graduate-level content from scratch? That felt like wandering through a haunted house to me. Dark, empty, and full of unknowns. Adding to the surrealness, I realized that thirteen years ago, I was a graduate student here, taking several Ecampus courses myself including an early version of the very class I would now be teaching. The idea that I could bring my professional experience back to this institution and shape this course? Thrilling, humbling… and a yes, definitely a little spooky.
The course, FES 454/554: Forestry in the Wildland-Urban Interface, explores the complex challenges of managing forests where communities and wildlands meet. Students dive into forest health, urban forestry, land-use planning, wildfire, and natural resource management through social, ecological, economic, and political lenses. It’s a “slash course,” meaning both undergraduates and graduate students can enroll so I knew the content needed to speak to a broad spectrum of learners. And I had to build it all from the ground up.
Enter the magical world of Ecampus Instructional Design. My Instructional Design partner was way more than support. To me, she was a friendly ghost guiding me through every room of this haunted course house. There were moments when I was convinced I had hit a dead-end, only to have a creative solution appear almost instantly. From turning complex assignments into clear, engaging experiences to keeping me on track and motivated, the team transformed my raw ideas into a cohesive, polished course. I honestly cannot say enough about the skill, creativity, and dedication they bring to the table.
One lesson I carried from my own hiking adventures literally proved invaluable during the course build. Years ago, I was struggling up a 14,000-foot peak in Colorado, staring at the distant summit, more than ready to quit. My hiking buddy simply said, “Don’t look at the summit. Pick a rock a few feet ahead and walk to that. Then take a break, and pick another rock.” That became my metaphor for course development. Instead of being paralyzed by the enormity of a ten-week course, I focused on the next “rock.” Some of my rocks included simply finishing the syllabus, creating the first assignment, securing a guest lecture, or finding a key reading. By breaking the work into manageable pieces, the haunted hallways of that blank course shell became far less intimidating and actually surprisingly rewarding.
Another highlight of building this course was connecting students with the people shaping forestry in the field. Reaching out to industry professionals for guest lectures and insights brought this material to life and grounded it in examples. It also reminded me how much real-world perspectives enrich student learning. Two colleagues from my department contributed individual weeks of material, which helped broaden the course and gave students a chance to see the WUI topic through multiple professional lenses. I was grateful for their contributions too! Seeing the course evolve into a bridge between theory and practice was incredibly rewarding and it reinforced a key principle I’d learned over the years through my various roles. That collaboration amplifies impact. Never has this resonated more with me!
For anyone stepping into a course development role for the first time, my advice is simple; Lean on the resources around you. The Ecampus team offers an incredible array of tools, templates, and guidance. Don’t hesitate to ask questions, tap into expertise, and stick to timelines. Above all, remember the “next rock” approach: the mountain is climbed one step at a time. Celebrate small wins along the way because they add up faster than you think.
Looking back, building this course has been a career highlight. From the panic of staring at a totally blank syllabus to the thrill of seeing assignments, discussions, and modules come alive, I’ve learned that teaching online is truly a team sport. The course may be called Forestry in the Wildland-Urban Interface, but what I really learned was how humans, collaboration, and thoughtful design intersect to create something extraordinary. I hope my story encourages other first-time developers to embrace the process, trust their teams, and find joy in the climb. After all, even a haunted course house is easier to navigate when you have friendly ghosts guiding the way and every “next rock” brings you closer to the summit. And as the crisp autumn air settles in and the leaves turn, I’m reminded that even the spookiest, most intimidating challenges can reveal unexpected magic when you face them step-by-step.
“You won’t always have a calculator in your pocket!”
How we laugh now, with calculators first arriving in our pockets and, eventually, smartphones putting one in our hands at all times.
I have seen a lot of comparisons 123 across the Internet to artificial intelligence (AI) and these mathematics classes of yesteryear. The idea being that AI is but the newest embodiment of this same concern, which ended up being overblown.
But is this an apt comparison to make? After all, we did not replace math lessons and teachers with pocket calculators, nor even with smart phones. The kindergarten student is not simply given a Casio and told to figure it out. The quote we all remember has a deeper meaning, hidden among the exacerbated response to the question so often asked by students: “Why are we learning this?”
The response
It was never about the calculator itself, but about knowing how, when, and why to use it. A calculator speeds up the arithmetic, but the core cognitive process remains the same. The key distinction is between pressing the = button and understanding the result of the = button. A student who can set up the equation, interpret the answer, and explain the steps behind the screen will retain the mathematical insight long after the device is switched off.
The new situation – Enter AI
Scenario
Pressed for time and juggling multiple commitments, a student turns to an AI tool to help finish an essay they might otherwise have written on their own. The result is a polished, well-structured piece that earns them a strong grade. On the surface, it looks like a success, but because the heavy lifting was outsourced, the student misses out on the deeper process of grappling with ideas, making connections, and building understanding.
This kind of situation highlights a broader concern: while AI can provide short-term relief for students under pressure, it also risks creating long-term gaps in learning. The issue is not simply that these tools exist, but that uncritical use of them can still produce passing grades without the student engaging in meaningful reflection gained by prior cohorts. Additionally, when AI-generated content contains inaccuracies or outright hallucinations, a student’s grade can suffer, revealing the importance of reviewing and verifying the material themselves. This rapid, widespread uptake stresses the need to move beyond use alone and toward cultivating the critical habits that ensure AI supports, rather than supplants, genuine learning.
Employing multivariate regression analysis, we find that students using GenAI tools score on average 6.71 (out of 100) points lower than non-users. While GenAI may offer benefits for learning and engagement, the way students actually use it correlates with diminished exam outcomes
Another study (Ju, 2023) found that:
After adjusting for background knowledge and demographic factors, complete reliance on AI for writing tasks led to a 25.1% reduction in accuracy. In contrast, AI-assisted reading resulted in a 12% decline. Ju (2023).
In this same study, Ju (2023) noted that while using AI to summarize texts improved both quality and output of comprehension, those who had a ‘robust background in the reading topic and superior reading/writing skills’ benefited the most.
Ironically, the students who would benefit most from critical reflection on AI use are often the ones using it most heavily, demonstrating the importance of embedding AI literacy into the curriculum. For example: A recent article by Heidi Mitchell from the Wall Street Journal (Mitchell, 2025) cites a study showing that the “less you know about AI, the more you are likely to use it”, and describing AI as seemingly “magical to those with low AI literacy”.
Finally, Kosmyna et al. (2025), testing how LLM usage affects cognitive processes and neural engagement in essay writing, assembled groups of LLM users, search engine users, and those without these tools (dubbed “brain-only” users). The authors recorded weaker performance in students with AI assistance over time, a lower sense of ownership of work with inability to recall work, and even seemingly reduced neural connectivity in LLM users compared to the brain-only group, which scored better in all of the above.
The takeaways from these studies are that unstructured AI use acts as a shortcut that erodes retention. While AI-assistance can be beneficial, outright replacement of thinking with it is harmful. In other words, AI amplifies existing competence but rarely builds it from scratch.
Undetected
Many people believe themselves to be fully capable of detecting AI-usage:
Most of the writing professors I spoke to told me that it’s abundantly clear when their students use AI. Sometimes there’s a smoothness to the language, a flattened syntax; other times, it’s clumsy and mechanical. The arguments are too evenhanded — counterpoints tend to be presented just as rigorously as the paper’s central thesis. Words like multifaceted and context pop up more than they might normally. On occasion, the evidence is more obvious, as when last year a teacher reported reading a paper that opened with “As an AI, I have been programmed …” Usually, though, the evidence is more subtle, which makes nailing an AI plagiarist harder than identifying the deed. (Walsh, 2025).
In the same NY Mag article, however, Walsh (2025) cites another study, showing that it might not be as clear who is using AI and who is not (emphasis added):
[…] while professors may think they are good at detecting AI-generated writing, studies have found they’re actually not. One, published in June 2024, used fake student profiles to slip 100 percent AI-generated work into professors’ grading piles at a U.K. university. The professors failed to flag 97 percent.
The two quotes are not contradictory; they describe different layers of the same phenomenon. Teachers feel they can spot AI because memorable extremes stick in their minds, yet systematic testing proves that intuition alone misses the overwhelming majority of AI‑generated work. This should not be surprising though, as most faculty have never been taught systematic ways to audit AI‑generated text (e.g., checking provenance metadata, probing for factual inconsistencies, or using stylometric analysis). Nor do most people, let alone faculty grading hundreds of papers per week, have the time to audit every student. Without a shared, college-wide rubric of sorts, detection remains an ad‑hoc, intuition‑driven activity. Faulty detection risks causing undue stress to students, and can foster a climate of mistrust by assuming that AI use is constant or inherently dishonest rather than an occasional tool in the learning process. Even with a rubric, instructors must weigh practical caveats: large-enrollment courses cannot sustain intensive auditing, some students may resist AI-required tasks, and disparities in access to tools raise equity concerns. For such approaches to work, they must be lightweight, flexible, and clearly framed as supporting learning rather than policing it.
This nuance is especially important when considering how widespread AI adoption has been. Walsh (2025) observed that “just two months after OpenAI launched ChatGPT, a survey of 1,000 college students found that nearly 90 percent of them had used the chatbot to help with homework assignments.” While this figure might seem to justify the use of AI detectors, it could simply reflect the novelty of the tool at the time rather than widespread intent to circumvent learning. In other words, high usage does not automatically equal cheating, showing the importance of measured, thoughtful approaches to AI in education rather than reactionary ones.
What to do…?
The main issue here is not that AI is magically writing better essays than humans can muster, it is that students are slipping past the very moments where they would normally grapple with concepts, evaluate evidence, and argue a position. Many institutions are now taking a proactive role rather than a reactive one, and I want to offer such a suggestion going forward.
Embracing the situation: The reflective AI honor log
It is a fact that large language models have become ubiquitous. They are embedded in web browsers, word processors, and even mobile keyboards. Trying to ban them outright creates a cat‑and‑mouse game; it also sends the message that the classroom is out of sync with the outside world.
Instead of fighting against a technology that is already embedded in our lives, invite students to declare when they use it and to reflect on what they learned from that interaction.
For this post, I am recommending using an “AI Honor-Log Document”, and deeply embedding it into courses, with the goal of increasing AI literacy.
What is it?
As assignments vary across departments and even within courses, a one-size-fits-all approach is unlikely to be effective. To support thoughtful AI use without creating extra work for students, faculty could select an approach that best aligns with their course design:
Built-in reflection: Students note when and how they used AI, paired with brief reflections integrated into their normal workflow.
Optional, just-in-time logging: Students quickly log AI use and jot a short note only when it feels helpful, requiring minimal time.
Embedded in assignments: Reflection is incorporated directly into the work, so students engage with it as part of the regular writing or research process.
Low-effort annotations: Students add brief notes alongside tasks they are already completing, making reflection simple and natural.
These options aim to cultivate critical thinking around AI without imposing additional burdens or creating the perception of punishment, particularly for students who may not be using AI at all.
AI literacy is a massive topic, so let’s only address a few things here:
Mechanics Awareness: Ability to explain the model architecture, training data, limits, and known biases.
Critical Evaluation: Requiring fact-checking, citation retrieval, and bias spotting.
Orchestration Skills: Understanding how to craft precise prompts, edit outputs, and add original analysis.
Note: you might want to go further and incorporate these into an assignment level learning outcome. Something like: “Identifies at least two potential biases in AI-generated text” could be enough on a rubric to gather interesting student responses.
Log layout example
#
Assignment/Activity
Date
AI Model
Exact Prompt
AI Output
What you changed/Added
Why You Edited
Confidence (1-5)
Link to Final Submission
1
Essay #2 – Digital-privacy law
2025-09-14
GPT-5
“Write a 250-word overview of GDPR’s extraterritorial reach and give two recent cases
[pastes AI text]
Added citation to 2023 policy ruling; re-phrased a vague sentence.
AI omitted the latest case; needed up-to-date reference
4
https://canvas.oregonstate.edu/……
Potential deployment tasks (and things to look out for)
It need not take much time to model this to students or deploy it in your course. That said, there are practical and pedagogical limits depending on course size, discipline, and student attitudes toward AI. The notes below highlight possible issues and ways to adjust.
Introduce the three reasons above (either text form or video, if you have more time and want to make a multimedia item). Caveat: Some students may be skeptical of AI-required work. Solution: Frame this as a reflection skill that can also be done without AI, offering an alternative if needed.
Distribute the template to students: post a Google-Sheet link (or similar) in the LMS. Caveat: Students with limited internet access or comfort with spreadsheets may struggle. Solution: Provide a simple Word/PDF version or allow handwritten reflections as a backup.
Model the process in the first week: Submit a sample log entry like the one above but related to your class and required assignment reflection type. Caveat: In large-enrollment courses, individualized modeling is difficult. Solution: Share one well-designed example for the whole class, or record a short screencast that students can revisit.
Require the link with each AI-assisted assignment (or as and when you believe AI will be used). Caveat: Students may feel burdened by repeated uploads or object to mandatory AI use. Solution: Keep the log lightweight (one or two lines per assignment) and permit opt-outs where students reflect without AI.
Provide periodic feedback: scan the logs, highlight common hallucinations or errors provided by students, give a “spot the error” mini lecture/check-in/office hour. Caveat: In large classes, it’s not realistic to read every log closely. Solution: Sample a subset of entries for themes, then share aggregated insights with the whole class during office hours, or post in weekly announcements or discussion boards designed for this kind of two-way feedback.
(Optional) Student sharing session in a discussion board: allow volunteers or require class to submit sanitized prompts (i.e., any personal data removed) and edits for peer learning. Caveat: Privacy concerns or reluctance to share work may arise. Solution: Keep sharing optional, encourage anonymization, and provide opt-outs to respect comfort levels.
Important considerations when planning AI-tasks
Faculty should be aware of several practical and pedagogical considerations when implementing AI-reflective logs. Large-enrollment courses may make detailed feedback or close monitoring of every log infeasible, requiring sampling or aggregated feedback. Some students may object to AI-required assignments for ethical, accessibility, or personal reasons, so alternatives should be available (i.e. the option to declare that a student did not use AI should be present). Unequal access to AI tools or internet connectivity can create equity concerns, and privacy issues may arise when students share prompts or work publicly. To address these challenges, any approach should remain lightweight, flexible, and clearly framed as a tool to support learning rather than as a policing mechanism.
Conclusion
While some students may feel tempted to rely on AI, passing an assignment in this manner can also pass over the critical thinking, analytical reasoning, and reflective judgment that go beyond content mastery to true intellectual growth. Incorporating a reflective AI-usage log based not on assumption of cheating, but on the ubiquitous availability of this now-common tool, reintroduces one of the evidence-based steps for learning and mastery that has fallen out of favor in the last 2-3 years. By encouraging students to pause, articulate, and evaluate their process, reflection helps them internalize knowledge, spot errors, and build the judgment skills that AI alone cannot provide.
Fu, Y. and Hiniker, A. (2025). Supporting Students’ Reading and Cognition with AI. In Proceedings of Workshop on Tools for Thought (CHI ’25 Workshop on Tools for Thought). ACM, New York, NY, USA, 5 pages. https://arxiv.org/pdf/2504.13900v1
Kosmyna, N., Hauptmann, E., Yuan, Y. T., Situ, J., Liao, X-H., Beresnitzky, A. V., Braunstein, I., & Maes, P. (2025). Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task. https://arxiv.org/abs/2506.08872
Fall Term is just around the corner, bringing with it new opportunities, fresh faces, and the chance to make a lasting impact on your students. Whether they’re logging in for the first time or for their final term, setting a welcoming and engaging tone from day one helps create a foundation for everyone’s success, yours included.
Here are a few ways to kick things off and set the stage for a smooth, successful term:
Start with a warm welcome
Post a welcome announcement and introduce yourself to your students.
Use a warm and welcoming tone in your message to help students feel encouraged, supported, and comfortable as they enter the course.
Personalize it with a photo or short video, it goes a long way in making connections.
Open your course early
If possible, open your course before the official start date. This gives students a chance to explore, order materials, and introduce themselves.
Open modules at least two weeks ahead. Many students juggle full-time jobs, families, and other commitments, so maximum flexibility is appreciated.
Keep communication open
Set up a Q&A discussion forum, and check it regularly. This allows you to answer common questions once and ensures everyone sees the response.
Encourage students to post questions in this forum and let students know when and how they can expect replies.
Be responsive to messages and follow up with students if needed.
Model engagement
Join discussion boards and post regularly. Ask guiding questions, offer feedback, or simply cheer students on, show them you’re present and engaged.
Think about how you’d engage in a face-to-face class and bring that energy to your online space too.
Be accessible
Hold regular office hours or offer flexible scheduling options. Creating the time and space for students to connect with you makes a difference.
Grade consistently and give meaningful feedback
Timely, constructive feedback helps students grow. The effort you put in early pays off in improved work later in the term.
Stay organized
Block out time in your calendar each week for class check-ins and grading. A little planning now can prevent overwhelm and burnout later.
Take care of yourself
Don’t forget to breathe. Support your students by also supporting yourself.
Be kind to yourself and set boundaries to attend to personal commitments, too.
Here’s to a strong, successful Fall Term — you’ve got this!
Core Education at Oregon State University launched summer 2025 and is designed to deepen how students think about problem-solving in ways that transcend disciplinary-specific approaches. It aims at preparing students to be adaptive, proactive members of society who are ready to take on any challenge, solve any problem, advance in their chosen career and help build a better world (Oregon State University Core Education, 2025).
Designing Seeking Solutions Signature Core category courses presents a few challenges, such as the nature of wicked problems, cross-discipline teamwork, and the global impact of wicked problems, to name just a few. In the past eight months, instructional designers at Oregon State University Ecampus have worked intensively to identify design challenges, brainstorm course design approaches, discuss research on teamwork and related topics, and draft guidelines and recommendations in preparation for the upcoming Seeking Solutions course development projects. Here is a list of the key topics we reviewed in the past few months. 1. Wick Problems 2. Team conflict 3. Online Large Enrollment Courses
Next, I will share summaries of research articles reviewed and implications for instructional design work for each of the above topics.
Wicked Problems
A wicked problem, also knowns as ill-structure problem or grand challenge, is a problem that is difficult or impossible to solve due to its complex and ever-changing nature. Research suggests that wicked problems must have high levels of three dimensions: complexity, uncertainty and value divergence. Complexity can take many forms but often involves the need for interdisciplinary reasoning and systems with multiple interacting variables. Uncertainty typically refers to how difficult it is to predict the outcome of attempts to address wicked problems. Value divergence refers particularly to wicked problems having stakeholders with fundamentally incompatible worldviews. It is the presence of multiple stakeholders in wicked problems with incompatible viewpoints that marks the shift from complex to super complex. (Veltman, Van Keulen, and Voogt, 2019; Head, 2008) The Seeking Solutions courses expect students to “wrestle with complex, multifaceted problems, and work to solve them and/or evaluate potential solutions from multiple points of view”. Supporting student learning using wicked problems involves designing activities with core elements that reflect the messiness of these types of problems. McCune et al. (2023) from University of Edinburgh interviewed 35 instructors teaching courses covering a broad range of subject areas. 20 instructors teaching practices focused on wicked problems, while the other 15 instructors whose teaching did not relate to wicked problems. The research goal is to understand how higher education teachers prepare students to engage with “wicked problems”—complex, ill-defined issues like climate change and inequality with unpredictable consequences. The research question is “Which ways of thinking and practicing foster effective student learning about wicked problems?” The article recommended four core learning aspects essential for addressing wicked problems from their study: 1. Interdisciplinary negotiation: Students must navigate and integrate different disciplinary epistemologies and values. 2. Embracing complexity/messiness: Recognizing uncertainty and non linear problem boundaries as part of authentic learning. 3. Engaging diverse perspectives: Working with multiple stakeholders and value systems to develop consensus-building capacities. 4. Developing “ways of being”: Cultivating positional flexibility, uncertainty tolerance, ethical awareness, and communication across differences
Applications for instructional designers:
As instructional designers work very closely with course developers, instructors, and faculty, they contribute significantly to the design of Seeking Solutions courses. Here are a few instructional design recommendations regarding wicked problems from instructional designers on our team: • Provide models or structures such as systems thinking for handling wicked problems. • Assign students to complete the Identity Wheel activity and reflect on how their different identities shape their views of the wicked problems or shifts based on contextual factors. (resources on The Identity Wheel, Social Wheel, and reflection activities). • Provide activities early in the course to train students on how to work and communicate in teams; to take different perspectives and viewpoints. • Create collaborative activities regarding perspective taking. • Evaluate assessment activities by focusing on several aspects of learning (students’ ability to participate; to solve the problem; grading the students on the ability to generate ideas, to offer different perspectives, and to collaborate; evaluation more on the process than the product, and self-reflection).
Team Conflict and Teamwork
“A central goal of this category is to have students wrestle with complex, multifaceted problems, and evaluate potential solutions from multiple points of view” (OSU Core Education, 2025). Working in teams provides an opportunity for teammates to learn from each other. However, teamwork is not always a straightforward and smooth collaboration. It can involve different opinions, disagreements, and conflict. While disagreements and differences can be positive for understanding others’ perspectives when taken respectively and rationally; when disagreements are taken poorly, differences in perspectives rises to become conflict and conflict could impact teamwork, morality, and outcomes negatively. Central to Seeking Solutions courses is collaborative teamwork where students will need to learn and apply their skills to work with others, including perspectives taking.
Aggrawal and Magana (2024) conducted a study on the effectiveness of conflict management training guided by principles of transformative learning and conflict management practice simulated via a Large Language Modeling (ChatGPT 3.5). Fifty-six students enrolled in a systems development course were exposed to conflict management intervention project. The study used the five modes of conflict management based on the Thomas-Kilmann Conflict Mode Instrument (TKI), namely: avoiding, competing, accommodating, compromising, and collaborating. The researchers use a 3-phase (Learn, Practice and Reflect) transformative learning pedagogy.
Learn phase: The instructor begins with a short introduction; next, students watch a youtube video (duration 16:16) on conflict resolution. The video highlighted two key strategies for navigating conflict situations: (1) refrain from instantly perceiving personal attacks, and (2) cultivate curiosity about the dynamics of difficult situations.
Practice phase: students practice conflict management with a simulation scenario using ChatGPT 3.5. Students received detailed guidance on using ChatGPT 3.5.
Reflect phase: students reflect on this session with guided questions provided by the instructor.
The findings indicate 65% of the students significantly increased in confidence in managing conflict with the intervention. The three most frequently used strategies for managing conflict were identifying the root cause of the problem, actively listening, and being specific and objective in explaining their concerns.
Application for Instructional Design
Providing students with opportunities to practice handling conflict is important for increasing their confidence in conflict management. Such learning activities should have relatable conflicts like roommate disputes, group project tension, in the form of role-play or simulation where students are given specific roles and goals, with structured after-activity reflection to guide students to process what happened and why, focusing on key conflict management skills such as I-messages, de-escalation, and reframing, and within safe environment.
Problem Solving
Creativity, collaboration, critical thinking, and communication—commonly referred to as the 4Cs essential for the future—are widely recognized as crucial skills that college students need to develop. Creative problem solving plays a vital role in teamwork, enabling teams to move beyond routine solutions, respond effectively to complexity, and develop innovative outcomes—particularly when confronted with unfamiliar or ill-structured problems. Oppert et al. (2022) found that top-performing engineers—those with the highest levels of knowledge, skills, and appreciation for creativity—tended to work in environments that foster psychological safety, which in turn supports and sustains creative thinking. Lim et al. (2014) proposed to provide students with real-world problems. Lee et al. (2009) suggest to train students on fundamental concepts and principles through a design course. Hatem and Ferrara (2001) suggest using creative writing activities to boost creative thinking among medical students.
Application for Instructional Designers
We recommend on including an activity to train students on conflict resolution, as a warm-up activity before students work on actual course activities that involve teamwork and perspective taking. Also, it will be helpful to create guidelines and resources that students can use for managing conflict, and add these resources to teamwork activities.
Large Enrollment Online Courses
Teaching large enrollment science courses online presents a unique set of challenges that require careful planning and innovative strategies. Large online classes often struggle with maintaining student engagement, providing timely and meaningful feedback, and facilitating authentic practice. These challenges underscore the need for thoughtful course design and pedagogical approaches in designing large-scale online learning environments.
Mohammed and team (2021) assessed the effectiveness of interactive multimedia elements in improving learning outcomes in online college-level courses, by surveying 2111 undergraduates at Arizona State University. Results show frequently reported factors that increase student anxiety online were technological issues (69.8%), proctored exams (68%), and difficulty getting to know other students. More than 50% of students reported at least moderate anxiety in the context of online college science courses. Students commonly reported that the potential for personal technology issues (69.8%) and proctored exams (68.0%) increased their anxiety, while being able to access content later (79.0%) and attending class from where they want (74.2%), and not having to be on camera where the most reported factors decreased their anxiety. The most common ways that students suggested that instructors could decrease student anxiety is to increase test-taking flexibility (25.0%) and be understanding (23.1%) and having an organized course. This study provides insight into how instructors can create more inclusive online learning environments for students with anxiety.
Applications for Instructional Design
What we can do to help reduce student anxieties in large online courses: 1. Design task reminders for instructors, making clear that the instructor and the school care about student concerns. 2. Design Pre-assigned student groups if necessary 3. Design warm up activities to help students get familiar with their group members quickly. 4. Design students preferences survey in week 1. 5. Design courses that Make it easy for students to seek and get help from instructors.
As Ecampus moves forward with course development, these evidence-based practices will support the instructional design work to create high-quality online courses that provide students with the opportunities to develop, refine, and apply skills to navigate uncertainty, engage diverse viewpoints, and contribute meaningfully to a rapidly changing world. Ultimately, the Seeking Solutions initiative aligns with OSU’s mission to cultivate proactive global citizens, ensuring that graduates are not only career-ready but also prepared to drive positive societal change.
Conclusions
Instructional design for solution-seeking courses requires thoughtful course design that addresses perspective taking, team collaboration, team conflict, problem solving, and possibly large enrollments. Proactive conflict resolution frameworks, clear team roles, and collaborative tools help mitigate interpersonal challenges, fostering productive teamwork. Additionally, integrating structured problem-solving approaches (e.g., design thinking, systems analysis) equips students to tackle complex, ambiguous “wicked problems” while aligning course outcomes with real-world challenges. Together, these elements ensure a robust, adaptable curriculum that prepares students for dynamic problem-solving and sustains long-term program success.
References
Aggrawal, S., & Magana, A. J. (2024). Teamwork Conflict Management Training and Conflict Resolution Practice via Large Language Models. Future Internet, 16(5), 177-. https://doi.org/10.3390/fi16050177
Bikowski, D. (2022). Teaching large-enrollment online language courses: Faculty perspectives and an emerging curricular model. System. Volume 105
Head, B. (2008). Wicked Problems in Public Policy. Public Policy, 3 (2): 101–118.
McCune, V., Tauritz, R., Boyd, S., Cross, A., Higgins, P., & Scoles, J. (2023). Teaching wicked problems in higher education: ways of thinking and practising. Teaching in Higher Education, 28(7), 1518–1533. https://doi.org/10.1080/13562517.2021.1911986
Mohammed, T. F., Nadile, E. M., Busch, C. A., Brister, D., Brownell, S. E., Claiborne, C. T., Edwards, B. A., Wolf, J. G., Lunt, C., Tran, M., Vargas, C., Walker, K. M., Warkina, T. D., Witt, M. L., Zheng, Y., & Cooper, K. M. (2021). Aspects of Large-Enrollment Online College Science Courses That Exacerbate and Alleviate Student Anxiety. CBE Life Sciences Education, 20(4), ar69–ar69. https://doi.org/10.1187/cbe.21-05-0132
Oppert ML, Dollard MF, Murugavel VR, Reiter-Palmon R, Reardon A, Cropley DH, O’Keeffe V. A Mixed-Methods Study of Creative Problem Solving and Psychosocial Safety Climate: Preparing Engineers for the Future of Work. Front Psychol. 2022 Feb 18;12:759226. doi: 10.3389/fpsyg.2021.759226. PMID: 35250689; PMCID: PMC8894438.
Veltman, M., J. Van Keulen, and J. Voogt. (2019). Design Principles for Addressing Wicked Problems Through Boundary Crossing in Higher Professional Education. Journal of Education and Work, 32 (2): 135–155. doi:10.1080/13639080.2019.1610165.
This post was written in collaboration with Mary Ellen Dello Stritto, Director of Ecampus Research Unit.
Quality Matters standards are supported by extensive research on effective learning. Oregon State University’s own Ecampus Essentials build upon these standards, incorporating OSU-specific quality criteria for ongoing course development. But what do students themselves think about the elements that constitute a well-designed online course?
The Study
The Ecampus Research Unit took part in a national research study with Penn State and Boise State universities that sought student insight into what elements of design and course management contribute to quality in an online course. Data was collected from 6 universities across the US including Oregon State in Fall of 2024. Students who chose to participate completed a 73-item online survey that asked about course design elements from the updated version of the Quality Matters Rubric. Students responded to each question with the following scale: 0=Not important, 1=Important, 2=Very Important, 3=Essential. A total of 124 students completed survey, including 15 OSU Ecampus students. The findings reveal a remarkable alignment between research-based best practices and student preferences, validating the approach taken in OSU’s Ecampus Essentials.
See the findings in data visualization form below, followed by a detailed description.
What Students Consider Most Important
Students clearly value practical, research-backed features that make online courses easier to navigate, more accessible, and more supportive of learning. The following items received the most ratings of “Essential” + “Very Important”:
QM Standards and Study Findings
Related Ecampus Essentials
Accessibility and Usability (QM Standards 8.2, 8.3, 8.4, 8.5, 8.6): Every OSU student rated course readability and accessible text as “Very Important” or “Essential” (100%). Nationally, this was also a top priority (96% and 91%, respectively). Accessibility of multimedia—like captions and user-friendly video/audio—was also highly rated (100% OSU, 90% nationally).
Text in the course site is accessible. Images in the course are accessible (e.g., alt text or long description for images). The course design facilitates readability. All video content is accurately captioned.
Clear Navigation and Getting Started (QM Standards 1.1, 8.1): 93% of OSU students and 94% of the national sample rated easy navigation highly, while 89% of OSU students and 96% nationally said clear instructions for how to get started and where to find things were essential.
Course is structured into intuitive sections (weeks, units, etc.) with all materials for each section housed within that section (e.g., one page with that week’s learning materials rather than a long list of files in the module). Course is organized with student-centered navigation, and it is clear to students how to get started in the course.
Meaningful Feedback and Instructor Presence (QM Standards 3.5, 5.3): Students placed high importance on receiving detailed feedback that connects directly to course content (100% OSU, 94% nationally). The ability to ask questions of instructors was also essential (100% OSU, 96% nationally).
Assessments are sequenced in a way to give students an opportunity to build knowledge and learn from instructor feedback. The instructor’s plan for regular interaction with students in substantive ways during the course is clearly articulated. Information about student support specific to the course (e.g., links to the Writing Center in a writing course, information about TA open office hours, etc.) is provided.
Clear Grading Criteria (QM Standards 3.2, 3.3): 93% of OSU students and the full sample found clear, detailed grading rules to be essential.
Specific and descriptive grading information for each assessment is provided (e.g., detailed grading criteria and/or rubrics).
Instructional Materials (QM Standard 4.1): All OSU students and 92% nationally rated high-quality materials that support learning outcomes as very important or essential.
Instructional materials align with the course and weekly outcomes. A variety of instructional materials are used to appeal to many learning preferences (readings, audio, visual, multimedia, etc.). When pre-recorded lectures are utilized, content is brief and integrated into course learning activities, such as with interactive components, discussion questions, or quiz questions. Longer lectures should be shortened to less than 20 min. chunks.
What Students Consider Less Important
The study also revealed areas where students expressed less enthusiasm:
Study Findings
Related Ecampus Essentials
Self-Introductions (QM Standard 1.9): Over half of OSU students (56%) and a third nationally (33%) rated opportunities to introduce themselves as “Not Important”.
No specific EE
Peer Interaction (QM Standard 5.2): Students were lukewarm about peer-to-peer learning activities. Nearly half said that working in small groups is not important (47% OSU, 46% nationally). About a quarter didn’t value sharing ideas in public forums (27% OSU, 24% nationally) or having learning activities that encourage them to interact with other students (27% OSU, 23% nationally).
Three forms of interaction are present, in some form, in the course (student/content, student/instructor, student/student).
Technology Variety and Data Privacy Info (QM Standards 6.3, 6.4): Some students questioned the value of using a variety of tech tools (20% OSU, 23% nationally rated this as “Not Important”) or being given info about protecting personal data (20% OSU, 22% nationally).
Privacy policies for any tools used outside of Canvas are provided.
Student Comments
Here are a few comments from Ecampus students that illustrate their opinions on what makes a quality course:
“Accessible instructional staff who will speak to students in synchronous environments. Staff who will guide students toward the answer rather than either treating it like cheating to ask for help at all or simply giving out the answer.”
“A lack of communication/response from teachers and no sense of community” – was seen as a barrier.
“Mild reliance on e-book/publisher content, out-weighed by individual faculty created content that matches student deliverables. In particular, short video content guiding through the material in short, digestible amounts (not more than 20 minutes at a go).”
“When there aren’t a variety of materials, it makes it hard to successfully understand the materials. For example, I prefer there to be lectures or videos associated with readings so that I understand the material to the professor’s standards. When I only have reading materials, I can sometimes misinterpret the information.”
“Knock it off with the discussion boards, and the ‘reply to 2 other posts’ business. This is not how effective discourse takes place, nor is it how collaborative learning/learning community is built.”
Conclusion and Recommendations
The takeaways? This research shows that students recognize and value the same quality elements emphasized in OSU’s Ecampus Essentials:
Student preferences align with research-based standards – Students consistently value accessibility, clear structure, meaningful feedback, and purposeful content.
Universal design benefits everyone – Students’ strong preference for accessible, well-designed courses supports the universal design principles embedded in the Ecampus Essentials.
However, there is always room for improvement, and these data provide some hints. Many students don’t immediately see value in peer interactions and collaborative activities, even though extensive educational research shows these are among the most effective learning strategies. Collaborative learning is recognized as a High Impact Practice that significantly improves student outcomes and critical thinking. This disconnect suggests we need to design these experiences more thoughtfully to help students recognize their benefits. Here are some suggestions:
Frame introductions purposefully: Instead of generic “tell us about yourself” posts, connect introductions to course content (“Introduce yourself and share an experience related to the topic of this course”).
Design meaningful group work: Create projects that genuinely require collaboration and produce something students couldn’t create alone.
Show the connection: Explicitly explain how peer interactions help students learn and retain information better, and the value of teamwork for their future jobs.
Start small: Begin with low-stakes peer activities before moving to more complex collaborations.
Over the past few years, Higher Education (HE) has been called to action in response to the rise of Generative Artificial Intelligence (GenAI) tools. As Artificial Intelligence (AI) becomes more autonomous and capable, proactive steps are needed to preserve academic and learning integrity. This article will explore tangible strategies educators can apply to their unique program and course contexts. Only slight adjustments may be necessary to support learning processes and capture evidence of learning, as changes will build upon the excellent work that is already occurring.
Initially, the focus in HE was on understanding the potential impact these tools would have on teaching and learning. Awareness of GenAI capabilities, limitations, and risks has been acknowledged with great care. Today, the tools are now being tested, and educators are envisioning how to use them for various purposes (e.g., productivity, creativity). Integration of these tools has begun with the aim of supplementing and enhancing human learning. As we move forward, concerns with regard to academic and learning integrity become increasingly prominent.
Meet Agentic AI
Recently, I had the opportunity to attend the Quality Matters Quality in Action conference, where I attended the session Ensuring Academic Integrity and Quality Course Design in the Age of AI. The presenter Robert Gibson, Director of Instructional Design at WSU Tech, shared about an Artificial Intelligence (AI) innovation now available to the public (and our students)….meet Agentic AI!
Your new Agentic AI assistant no longer requires you to be an expert prompt engineer. These tools are designed to achieve specific and clear goals with minimal human supervision or oversight. Engagement in complex reasoning, decision making, problem solving, learning from new information, and adapting to environments can occur autonomously (Gibson, 2025; Schroeder, 2025; Marr, 2025). These new Agentic AIs can even work together to form what is known as an Orchestrated AI. Think of this as an AI team working collaboratively to accomplish complex tasks. Agentic AI has already demonstrated the capability to create and complete online courses. What does this mean for Higher Education?
Now more than ever, we need to come together to collectively reinforce academic and learning integrity in online and hybrid courses. Preserving the quality of our institutional products and credentials is essential. Equally important are the students who will apply their OSU-acquired knowledge and skills in the real world. The time to be proactive is now.
Where and how should I start?
A good starting point is to evaluate assessments that AI can complete. Running an assignment through a GenAI tool to see if it can complete the task, with relative accuracy, can produce helpful insights. Next, consider modifications to pedagogical approaches and assessment methods. The goal is to design assessments to produce and capture evidence that learning is taking place. This could include assessments that are process-oriented, focus on skill mastery, are personalized, incorporate visual demonstrations (e.g., video), and/or integrate real-time engagement (Gibson, 2025).
What might a reimagined activity look like?
For example, let us say an instructor uses case-based learning in their course, and small groups discuss real-world scenarios on a discussion board. This activity could be reimagined by having students meet virtually and record their discussion. During their real-time interaction, they examine a real-world scenario, identify associated evidence, present examples, and share their lived experiences. This would be similar to how students conduct group presentations currently. This approach could be enhanced by shifting the focus to the learning process, such as arriving at ideas and cultivating perspectives (i.e., learning, growth, development). This would be in lieu of having students find a right or wrong answer (Gibson, 2025). This approach encourages students to engage substantively, co-construct knowledge, and work together to demonstrate learning. After participating in the activity, each student could create an individual video presentation to synthesize their learning. A synthesis video could include discussing their initial perspectives (Where did I start? – prior knowledge activation), how those initial perspectives evolved (What was my cognitive process? – metacognition), what new knowledge is needed (gap analysis), and how my perspectives and knowledge change (learning reflection). This method reinforces academic and learning integrity by validating that students are learning and achieving outcomes (Bertram-Gallant, 2017).
Reflect! Take a moment to reflect on how you know that students are learning in your course(s). What evidence do you have?
While the potential for academic dishonesty cannot be entirely controlled, there should not be an assumption that students will use these tools in their coursework just because they are available. Take a moment to examine the Ecampus Research Unit’s research, “Student Perceptions of Generative AI Tools in Online Courses.” This research study explores online students’ perceptions, understanding, and use of GenAI tools. The study found that most students had not been using GenAI tools in their courses, but rather, they were primarily using GenAI tools within professional contexts. Students noted that they understood that using AI in their careers would be necessary. However, strong concerns were articulated around inaccuracies, biases, lack of reliability, propagation of misinformation, and that the use of the tools is not in alignment with their personal values and ethics. (Dello Stritto, M, Underhill, G. and Aguiar, N., 2024).
How can academic and learning integrity be reinforced?
Educators can foster academic integrity in a way that drives students’ internal motivation, self-determination, and desire to demonstrate their learning because they value the work they are doing. A multifaceted developmental approach that fosters a culture of academic integrity using various strategies in concert with one another is key (Bertram-Gallant, 2025), as no single approach can serve as a definitive solution.
Integrity teaching – Taking on the role of an active guide during course delivery and meeting students where they are developmentally is essential. This may include teaching students how to engage in critical thinking around the use of AI tools, connect the value of academic and learning integrity to their future profession, how to make well-informed decisions, and how to leverage metacognitive strategies when engaging with AI.
Integrity messaging – This approach is one that can be most effective if holistically integrated into a course. The content communicates that integrity, values, and ethics are normative within the course and will be held at the forefront of the learning community. Staged and timed messaging can be most helpful when targeted at different points in a course and as the complexity of academic work increases.
Transformative real time experiential learning – Transformative experiential learning involves designing opportunities that generate new ideas for action, which can be applied to other experiences. These activities may include, but are not limited to, service learning, internships, hands-on collaborative activities (e.g., role play, point-counterpoint discussions), and demonstrations. By focusing on real-time engagement, this approach demonstrates learning and thereby reinforces academic and learning integrity.
Deep learning – Learning opportunities focused on skill mastery and demonstration through staged attempts. This approach may necessitate a pedagogical shift focusing on development and growth (Bertram-Gallant, 2025).
Agentic AI brings exciting opportunities for the world but tangible challenges for HE. By intentionally designing assessments that lead students to demonstrate evidence of their learning and using facilitation strategies that foster a culture of academic integrity, we can harness the potential of AI to supplement learning. What is the end goal? To ensure that educational opportunities are designed to preserve and enhance learners’ critical skills and knowledge needed to thrive in their professional pursuits. Will you accept this challenge?
Trying to decide when and how to incorporate AI into your work? Take a look at the AI Decision Tree!
Need a few quick, practical strategies to get started? These recommendations aim to improve learning for both teachers and students.
Are you ready to evaluate and enhance the resiliency (i.e., flexibility, adaptability) of your course within the context of AI? Check out the new The Course AI Resilience Tracker [CART] interactive tool. This interactive tool can help you reflect on various course elements and will share personalized resources to help you get started.
Review Bloom’s Taxonomy Revisited to explore how to emphasize distinctive human skills and/or integrate AI tools to supplement the learning process.
Explore our AI Assessment Examples Library for assessment ideas designed to incorporate AI tools and strategies in your course and/or create more human-centric assessments.
Bertram Gallant, T. & Rettinger, D. (2025, March 11). The Opposite of Cheating: Teaching for Integrity in the Age of AI. University of Oklahoma Press.
Dello Stritto, M. E., Underhill G. R., & Aguiar, N. R. (2024). Online Students’ Perceptions of Generative AI. Oregon State University Ecampus Research Unit. https://ecampus.oregonstate.edu/research/publications/
Gibson, R. (2025, April 10). Ensuring Academic Integrity and Quality Course Design in the Age of AI [Conference presentation]. Quality Matters Quality in Action 2025. Virtual.
Ashlee M. C. Foster, MSEd, is a seasoned Instructional Designer with the Oregon State University Ecampus Course Development and Training Team. With a profound commitment to supporting faculty and students in online teaching and learning, Ashlee’s mission is to design high-quality and innovative educational opportunities that foster transformational learning, development, and growth. Ashlee’s learning design approaches are grounded in research-based insights, foundational learning theories, and the thoughtful integration of industry-led practices. This ensures that each educational experience is not only effective but also engaging and relevant.
Giving and receiving feedback effectively is a key skill we all develop as we grow, and it helps us reflect on our performance, guide our future behavior, and fine-tune our practices. Later in life, feedback continues to be vital as we move into work and careers, getting feedback from the people we work for and with. As teachers, the most important aspect of our job is giving feedback that informs students how to improve and meet the learning outcomes to pass our courses. We soon learn, however, that giving feedback can be difficult for several reasons. Despite it being one of our primary job duties as educators, we may have received little training on how to give feedback or what effective feedback looks like. We also realize how time-consuming it can be to provide detailed feedback students need to improve. To make matters worse, we may find that students don’t do much with the feedback we spend so much time providing. Additionally, students may not respond well to feedback- they might become defensive, feel misunderstood, or worse, ignore the feedback altogether. This can set us up for an ineffective feedback process, which can be frustrating for both sides.
I taught ESL to international students from around the world for more than 10 years and have given a fair amount of feedback. Over many cycles, I developed a detailed and systematic approach for providing feedback that looked like this.
Gaps in this cycle can lead to frustration from both sides. Each step in the cycle is essential, so we’ll look at each in greater depth in this blog series. Today, we will focus on starting strong by preparing students to receive feedback, a crucial beginning that sets the stage for a healthy cycle.
Step 1: Prepare Students to Receive Feedback
An effective feedback cycle starts before the feedback is given by laying careful groundwork. The first and often-overlooked step in the cycle is preparing students to receive feedback, which takes planned, ongoing work. Various factors may influence whether students welcome feedback, including their self-confidence going into your course, their own self-concept and mindset as a learner, their working memory and learning capacity, how they view your feedback, and whether they feel they can trust you. Outside factors such as motivation and working memory are often beyond our control,butcreating an atmosphere of trust and safety in the classroom can positively support students. Student confidence and mindset are areas in which teachers can play a crucial supporting role.
Researcher Carol Dweck coined the term “growth mindset” after noticing that some students showed remarkable resilience when faced with hardship or failure. In contrast, others tended to easily become frustrated and angry, and tended to give up on tasks. She developed her theory of growth vs. fixed mindsets to explain and expound on the differences between these two mindsets. The chart below shows some of the features of each extreme, and we can easily see how a fixed mindset can limit students’ resilience and persistence when faced with difficulties.
Mindset directly impacts how students receive feedback. Research has shown that students who believe that their intelligence and abilities can be developed through hard work and dedication are more likely to put in the effort and persist through difficult tasks, while those who see intelligence as a fixed, unchangeable quality are more likely to see feedback as criticism and give up.
Developing a growth mindset can have transformative results for students, especially if they have grown up in a particularly fixed mindset environment. People with a growth mindset are more likely to seek out feedback and use it to improve their performance, while those with a fixed mindset may be more likely to ignore feedback or become defensive when receiving it. Those who receive praise for their effort and hard work, rather than just their innate abilities, are more likely to develop a growth mindset. This is because they come to see themselves as capable of improving through their own efforts, rather than just relying on their natural talents. A growth mindset also helps students learn to deal with failure and reframe it positively. It can be very difficult to receive a critique without tying our performance to our identity. Students must have some level of assurance that they will be safe taking risks and trying, without fear of being punished for failing.
Additionally, our own mindset affects how we view student effort, and we often, purposefully or not, convey those messages to students. Teachers with growth mindsets have a positive and statistically significant association with the development of their students’ growth mindsets. Our own mindset affects the type of feedback we are likely to provide, the amount of time we spend on giving feedback, and the way we view the abilities of our students.
These data suggest that taking the time to learn about and foster a growth mindset in ourselves and our students results in benefits for all. Teachers need to address the value of feedback early on in the learning process and repeatedly throughout the term or year, and couching our messaging to students in positive, growth-oriented language can bolster the feedback process and start students off on the right foot, prepared to improve.
Here are some concrete steps you can take to improve how your students will receive feedback:
Model a growth mindset through language and actions
Include growth-oriented statements in early messaging
Provide resources for students to learn more about growth vs. fixed mindsets
Discuss the value of feedback and incorporate it into lessons
Create an atmosphere of trust and safety that helps students feel comfortable trying new things
Teach that feedback is NOT a judgment of the person, but rather a judgment on the product or process
Ensure the feedback we give focuses on the product or process rather than the individual
Praise effort rather than intelligence
Make it clear that failure is part of learning and that feedback helps improve performance
Provide students with tools and strategies to plan, monitor, and evaluate their learning
Resources for learning more about growth mindset and how it relates to feedback:
One of my favorite design strategies is to make a small adjustment that delivers a big impact. When it comes to creating a welcoming online course, certain small adjustments can do just that and go a long way in warming up the online classroom. But first, let us look at why online courses ought to be welcoming and then what it means to be welcoming in the online space.
Why Welcoming Students Is Important
First, why is it important to design a welcoming course? According to the OSU Ecampus Online Teaching Principles, which are supported by research and endorsed by Quality Matters, it is recommended to “[m]ake facilitation choices that support diverse students and make each student feel welcomed and valued.” Additionally, specific review standard 1.8 from the Quality Matters Higher Education Rubric, 7th Edition, states that “The self-introduction by the instructor is welcoming and is available in the course site.” Furthermore, UDL 3.0 Guidelines were updated recently and include “Design Options for Welcoming Interests & Identities.” While all of those are evidence-based recommendations, I think it is safe to say that many faculty also have plenty of anecdotal evidence for the benefits to students when feeling welcome in a course. If one has been working in the higher education context for several years, it is easy to forget that many students struggle to feel that they belong in this context. Warm communication and greetings is one way to begin connecting with students who are skeptical that their experience matters or that their presence is valued.
What Does It Mean To Be Welcoming?
Next, let’s look at what it means to be welcoming in the online classroom. If we get down to basics and turn to a dictionary definition, we see that Merriam-Webster has defined welcoming as “to greet hospitably and with courtesy and cordiality; to accept with pleasure the occurrence or presence of.” For the online modality then, we can ask ourselves some questions: Where in the course can facilitators greet students? When students inquire about office hours or email with a question, can their presence be warmly accepted? Next let’s look at actions that faculty or other course facilitators, such as graduate teaching assistants, can take to be welcoming.
Creating a Welcoming Online Classroom
The following tips are just a few of the actions that can be taken to create a welcoming online classroom:
Greet each student in the introduction discussion. Replying to each student is one of those actions that is small but has a big impact.
Many online students are older than the traditional college age, so they often have extensive work experience and life experiences to draw upon. Acknowledging this life experience can go a long way in welcoming students.
Rename office hours to something like Coffee Chat, Afternoon Tea, or Q&A Hour. Here is an example to consider: Which description of office hours sounds more welcoming, Example 1 or Example 2? Example 1: “Office Hours are held by appointment. Please email to make an appointment.” Let’s compare that to Example 2: “Please join me for a Coffee Chat this term! Coffee Chats are held three times per term, as an open Zoom room for our class. If you can’t attend any of the scheduled Coffee Chats, please email me and schedule a time to meet. I want to get to know each of you. Furthermore, when I get to know students, I am better positioned to serve as a reference for educational or professional opportunities that come up in our field. I look forward to meeting with you!”
Consider how students are described in the course site. Alternative descriptions besides “students” could be fellow scholars, colleagues, participants, etc.
Consider these two different introduction discussion prompt designs, 1 and 2:
Design 1: “Students: Post an introduction that includes the following: Your major and why you are taking this course. Reply to two other students.”
Design 2 (designed to be more welcoming): “Welcome, fellow engineering scholars! Please introduce yourselves so that we may all begin to get to know each other. In your post, include 1) an educational or professional goal that you have connected to this course, 2) a time management tip that you have found helpful that you are willing to share with others, and 3) a photo or fun fact about yourself. Replies to other participants are optional but encouraged.”
Ecampus Online Teaching Principles, endorsed by Quality Matters, recommend “referring to each student by name with their chosen pronouns.” Sometimes students who use a shortened nickname will say so in their introductory post, but it is also nice to include instructions for students on how to change their display name in the course site. That way, facilitators of the course, including graduate teaching assistants, if applicable, do not have to refer back to the introduction post to remember what students prefer to be called.
Takeaway
Adding a welcoming tone to a course does not mean that the whole course needs to be redesigned. A few small adjustments here and there can make a difference.
“In the Winter Term 2024, the Ecampus Research Unit conducted a survey study of 669 students who had taken online courses at OSU. The 40-item survey was designed to assess students’ knowledge and use of generative AI tools, as well as their perceptions of their use in their courses and careers. A full report of this study is available on the Ecampus Research Unit website. Based on the results of this study, several recommendations were developed to guide decision making about generative AI tools in online courses.”
Dello Stritto, Underhill, & Aguiar (2024).
This recent study highlighted three key recommendations for faculty seeking to integrate generative AI into their courses effectively:
Recommendation 1
Write a course policy about generative AI that is clearly explained.
Recommendation 2
Consider a wide range of student emotions and concerns when integrating generative AI in your online courses.
Recommendation 3
Educate students on generative AI tools.
Applying data to design
To apply these recommendations in practice, we can reorganize them into instructional design categories that foster AI resiliency in course design: Course Learning Outcomes, Learner Profiles, Learning Materials, Activities and Assessments, and Course Policies. These categories offer a comprehensive framework for integrating AI while addressing students’ concerns and enhancing learning experiences.
Course Policies: Establish Clear Guidelines for AI Usage
Reflecting Recommendation 1, developing a clear, transparent policy on AI usage is key. Faculty should articulate when and how students can use AI tools, providing specific examples of ethical use. By defining these expectations early in the course, instructors help students understand the role AI can play in their learning process, promoting academic integrity.
Learner Profiles: Address Emotional and Academic Concerns
In line with Recommendation 2, it is essential to consider students’ diverse reactions to AI—ranging from excitement to anxiety—when designing a course. This is where understanding Learner Profiles becomes critical.
Learning Materials and Activities: Ensure Relevance and Adaptability with AI
Recommendation 3 emphasizes the importance of educating students about generative AI, which can be achieved through thoughtful integration into learning materials, activities, and assessments.
Course Learning Outcomes: Integrate AI with Intentional Learning Design
The integration of generative AI tools into course design necessitates an examination of their impact on student mastery of the Course Learning Outcomes. It is vital to ensure that student use of AI tools supplement and enhance the learning process rather than bypass cognitive engagement.
With these four considerations in mind, we can now introduce a tool to help assess and improve course resilience against generative AI, while providing learners with clear policy decisions and explanations.
Introducing CART: Course AI Resiliency Tracker
In response to the clear need for effective integration of generative AI in educational settings, a new tool has been developed (as part of a wider suite of artificial intelligence tools) to assist faculty in navigating this complex landscape. This tool is designed to support instructors in evaluating how generative AI could respond to their course learning outcomes by highlighting its current capabilities to address and complete these outcomes. It facilitates a detailed understanding of learner profiles to ensure that AI applications are relevant and accessible to all students. Additionally, the tool encourages faculty to reflect on the currency and relevance of their learning materials and to assess how AI might be incorporated into activities and assignments. By examining existing course policies on AI usage and offering actionable steps for course development, this resource aims to demystify generative AI for both educators and students, promoting a thoughtful and strategic approach to its integration or decision to restrict AI.
Getting Started
Upon accessing the landing page, you will be prompted to input your Course ID, after which you may proceed by selecting the “Start” button.
Learning Outcomes
The first step in the tool involves a reflection on your Course Learning Outcomes (CLOs). At this stage, you will have the option to choose from a list of commonly used learning outcome verbs, organized by the general categories of Bloom’s Taxonomy. Note that there is a current selection limit of five CLOs at one time, and faculty with verbs absent from this list are encouraged at this time to select verbs that are most like those in their own CLOs to get feedback that will feel the most transferable.
After selecting the appropriate verbs that align with your outcomes, click on the “Test Resiliency” button. This will display feedback on how generative AI may already be able to meet expectations for common tasks associated with those action verbs.
Your Learners
Following the assessment of CLOs, the next step encourages you to consider your learners. In this section, you are invited to input relevant details about your students, including their backgrounds, career aspirations, prior knowledge, or any other contextual information that could inform your generative AI course policies. We are aware that this question might feel challenging, especially for faculty who teach all kinds of learners as part of a general education course. In this case, consider this as a more general introduction to the wide variety of learner profiles that may take the course, and how generative AI may be used from their perspective.
Your responses here, as with all inputs in the tool, will be temporarily stored and displayed on the Summary Page for your future reference.
Learning Materials
Next, the tool asks you to evaluate the relevance and adaptability of your learning materials. You may choose from the pre-set options provided, or alternatively, you can select “Other” to add customized choices based on your specific course materials.
Activities and Assessments
Next, you will be prompted to reflect on your course activities and assessments. This section includes three key questions. Two of the questions are straightforward yes-or-no inquiries, while the third invites you to select one or more methods that you currently employ to promote academic integrity in your assessments. Including this information alongside activities and assessments bolsters understanding for your learners about expected Gen AI usage, why the choice has been made, and enhances academic integrity across the entire course.
Course Policies
You will then be prompted to consider an important question: does your syllabus currently include a policy on generative AI? This reflection is crucial for ensuring transparency and consistency in how AI is addressed throughout your course design. After choosing one of the answers, you will be able to select from some key elements to include in your AI usage policy.
Next Steps
Finally, the tool concludes by prompting you to consider the next steps in your course development, offering guidance on how to proceed with integrating generative AI effectively. Each choice offers different recommendations as automatic feedback, and you are encouraged to read through them all before moving onto the final summary.
Summary Page
At the conclusion of the tool, you will be directed to a Summary Page that consolidates all your previous inputs, along with the guidance and recommendations provided throughout the process. This comprehensive summary can be printed or saved as a PDF for future reference and review.
The benefits of using the tool
Recommendation 1: A clearly explained course policy
The new tool supports this recommendation by guiding instructors to design course policies that offer clear instructions to learners on what is allowed and disallowed, and most importantly to give rationales behind these policy decisions.
Recommendation 2: Considering learner profiles
The tool helps instructors map these profiles to ensure that generative AI is integrated in ways that are accessible, equitable, and aligned with the emotional and cognitive needs of different students. By anticipating student concerns, instructors can provide thoughtful guidance on how AI will or will not be used in various course activities and assessments.
Recommendation 3: Ensure Relevance and Adaptability with AI
The tool helps instructors evaluate the relevance and adaptability of their current materials by offering pre-set options or the ability to add customized choices. This process ensures that course content remains up-to-date and flexible enough to incorporate generative AI effectively or alternatively, provides avenues to secure assessments against AI generated content.
Course Learning Outcomes: Integrate AI with Intentional Learning Design
The tool supports this by guiding instructors through a reflection on their CLOs, offering a selection of commonly used learning outcome verbs categorized by Bloom’s Taxonomy. It also helps educators recognize the extent to which generative AI can currently accomplish many of these learning outcomes, providing valuable insights into the specific areas where AI might enhance or support course goals. the purpose of this is to ensure that AI integration choices are not just incidental, but strategically aligned with fostering critical thinking, creativity, and problem-solving skills within the broader context of your course objectives.
Conclusion
In response to the growing need for effective AI integration, this new tool helps faculty navigate the complexities of incorporating generative AI into course design. By addressing Course Learning Outcomes, Learner Profiles, Learning Materials, Activities and Assessments, and Course Policies, the tool promotes a strategic approach that aims to demystify AI for both educators and students. With thoughtful integration, well-designed generative AI policies can enhance learning experiences, help prepare students for future, teach learners to avoid potential pitfalls, and maintain the academic integrity of online courses.
MTH 112Z at Oregon State University is designed to prepare students for calculus and related disciplines. This course explores trigonometric functions and their applications as well as the language and measurement of angles, triangles, circles, and vectors. These topics are explored symbolically, numerically, and graphically in real-life applications. MTH 112Z is designated as a Common Course Numbering (CCN) in the state of Oregon, ending with “Z” in the course number. When transferring to an Oregon public college or university, “CCN courses will be accepted as if they were taken at the institution students transfer to (that is, the receiving institution)” (State of Oregon, 2023).
An instructor from the math department and Tianhong Shi from Ecampus collaborated in designing a brand-new version of MTH 112 to meet the new Core Ed requirements for Oregon State University and Z course requirement for the state of Oregon. At the beginning of this project, the design team identified major challenges of this course as follows: 1. Content challenges 2. Low motivation for some students to continue studying math at this level after initial frustration in this course. 3. Low interest in participation in class discussions.
The instructor and Tianhong met regularly to discuss the challenges, brainstorm strategies for solutions, and delineate a plan to implement practical solutions for MTH 112Z. The solutions that were implemented in the course include:
1. Creating a safe and inclusive learning environment that students will feel they belong here. 2. Creating short animated stories of how math operates in people’s real life, each video is about or less than 30 seconds long. The purpose of these animations is to build a bridge between math learning and real life and to motivate students to learn the topics of each unit. 3. Helping students to identify the steps in solving a math problem to scaffold learning and build learning success step by step. 4. Creating “Make Learning Fun” discussion topics: Research (Purinton and Burke, 2019; Tews, et.al., 2014) tells us that when students feel emotionally relaxed and happy, learning is more effective. Therefore, one “Make Learning Fun” discussion forum is created for each unit.
Building an Inclusive and Trusting Learning Community where Students Belong College belonging is defined as “students’ perceived social support on campus, a feeling or sensation of connectedness, the experience of mattering or feeling cared about, accepted, respected, valued by, and important to the group”, according to Strayhorn (2018, p.4). The strategies used to build an inclusive and trusting learning community in MTH 112Z included the following: 1. In Start Here Module, the instructor made a video covering Artificial Intelligence (AI), academic integrity, honesty, and diversity, to explicitly explain the expectations for this course regarding academic integrity and why it is so. 2. Also in Start Here Module, the instructor built a “Name Tents 112Z” discussion board for students to introduce themselves, setting an example by introducing the instructor himself first. 3. There is a Diversity Forum where students can post comments that they would want the instructor to know about themselves to make learning more inclusive.
Making Content Relevant In addressing the challenging content, the instructor identified concepts that would be better explained through a set of short animated videos, recorded the audio narratives, and the media team helped creating the short animations. For example, at the beginning of unit 4 is an animation about finding the length of a tall tree on campus. And here is the transcript of the video: “The Trees on the O S U campus, are wonderful . how tall are the cedar trees by the memorial union? if you measure the angle from the ground to the top of the tree and know the distance you’re standing away from the tree, you can compute it. Make a triangle and set up an equation to get the height. Which function would you use?” And here is the transcript of unit 5 animation video: “You can get swept away in a river. Oregon has many great rivers for boating. When you were kayaking, you need to account for how much the current will push you off course, this can be done with vector. One vector represents the river’s flow with direction and strength, another vector is the direction which you kayak. The results of these two added together is the direction you end up going. If you want to reach a certain point on the other side, where should you aim?” We can see from these two examples that they are relevant to student lives (trees and kayaking) and relevant to the topics of the units. And these animations tell short stories, hoping to motivate students for learning.
Scaffolding Toward Learning Success Scaffolded learning activities provide students a supportive learning environment (Dennen, 2004). In each unit’s content discussion forum and homework assignment, students explore problem solving step by step and discuss with each other to help them build confidence and fluency in problem solving. By such a design, the design team hoped students would get the support they needed and would be able to easily identify where they did wrong and how to improve or correct based on the feedback they receive from the online homework system and from the instructor and Teaching Assistants.
Making Learning Fun Emotional health is important for students’ learning success. Research suggests there is a significant positive relation between fun delivery of content and the forms of engagement (Tews, et al., 2014). Schwartz et al. (2016) also recommend building fun elements in learning for effective teaching and learning. So the design team strived to build elements of fun into the course. The short animations are meant for fun. In addition, each unit has a “Just For Fun” discussion forum to bring students’ attention to learning and promote motivation. Below are examples of these discussions:
Unit 1 Just for Fun: Please read through this survey and describe how you would answer the questions. (The survey was about having students imagine themselves navigating through the forest on foot and trying to find their way to their cabin.)
Unit 2 Just for Fun: What do you think of the animation?
Unit 3: Just for Fun: Please take a picture of something you can model with a sine function as you have been studying in this module. It could be a windmill if you live near a windmill, or an ocean if you live near an ocean. Make sure it is a picture that you have taken and then explain briefly what it is and how you would model its movement.
Unit 4 Just for Fun: Describe a time when you could feel the effect of the wind or water current as you were moving. For example, winters in Oregon are blustery and you can get blown around when you are biking. Or you can describe a way that you would use vectors in your own life.
Unit 5 Just for Fun: This is it! you’re almost done– What was a topic in the course that was interesting to you? or what was a topic that didn’t seem to be useful?
That is what we did to make introductory college math fun, inclusive and learnable. If you have ideas for math or STEM course design, feel free to share with us (Tianhong.shi@oregonstate.edu). The more, the better!
References Dennen, V. P. (2004). Cognitive apprenticeship in educational practice: Research on scaffolding, modeling, mentoring, and coaching as instructional strategies. In D. H. Jonassen (Ed.), Handbook of Research on Educational Communications and Technology (2nd ed.), (p. 815). Mahwah, NJ: Lawrence Erlbaum Associates.
Hogan, K., and Pressley, M. (1997). Scaffolding student learning: Instructional approaches and issues.Cambridge, MA: Brookline Books.
Huck, C and Zhang, J., Efects of the COVID-19 Pandemic on K-12 Education: A Systemic Literature Review. Educational Research and Development Journal. Summer 2021, Vol. 24.
Purinton, E. and Burke, M. (2019). Student Engagement and Fun: Evidence from the Field. Business Education Innovation Journal, Volume 11 Number 2, P133-P140.
Schwartz, D. L., Tsang, J. M., & Blair, K. P. (2016). The ABCs of how we learn : 26 scientifically proven approaches, how they work, and when to use them (First edition.). W.W. Norton & Company, Inc.
Strayhorn, T. L. (2018). College students’ sense of belonging. Routledge. https://doi-org.oregonstate.idm.oclc.org/10.4324/9781315297293
Tews, M. J., Jackson, K., Ramsay, C., & Michel, J. W. (2014). Fun in the college classroom: Examining its nature and relationship with student engagement. College Teaching, 63(1), 16-26.
