In the previous post, I gave an assignment prompt to Copilot (as that’s the recommended tool at Oregon State University) and asked it to complete the task. For reference, here is the task.
Rubrics are often the weakest link in assessment design, particularly when descriptors rely on vague phrases like “meets expectations” or “demonstrates understanding.” One way to evaluate rubric clarity is to ask AI to self-assess its own response using the rubric criteria.
If the model can plausibly justify a high score despite shallow reasoning or inconsistent logic, the rubric may not be clearly distinguishing levels of performance. More precise rubrics specify what evidence matters and how quality differs, emphasizing reasoning, coherence, and alignment with course concepts rather than polish or length. Clear criteria benefit students, but they also make it harder for superficially strong work to masquerade as deep learning.
Rubric Analysis Prompt (Click to expand)
You are now acting as an external assessment reviewer, not a student. You will be given:
An assignment prompt
A grading rubric
A model-generated student submission (your own prior response)
Your task is not to grade the submission. Instead, critically evaluate the rubric itself by answering the following:
Rubric Vulnerabilities
Identify specific rubric criteria or descriptors that allow a high score to be justified through fluent but shallow reasoning.
For each vulnerability, explain what kind of weak or superficial evidence could still plausibly receive a high score under the current wording.
Distinguishing Performance Levels
For at least three rubric categories, explain why the difference between “Excellent” and “Good” (or “Good” and “Satisfactory”) may be ambiguous in practice.
Describe what concrete evidence a human grader would need to reliably distinguish between those levels.
AI Self-Assessment Stress Test
Using your own generated submission as an example, explain how it could convincingly argue for a high score even if underlying understanding were limited.
Point to specific rubric language that enables this justification.
Rubric Strengthening Recommendations
Propose revised rubric language that makes expectations more explicit and evidence-based.
Emphasize observable reasoning, causal explanation, constraint awareness, or conceptual boundaries rather than general phrases such as “demonstrates understanding” or “well-justified.”
Constraints:
Do not rewrite the assignment prompt.
Do not assume access to course-specific lectures or materials.
Focus on how the rubric functions as an assessment instrument, not on pedagogy or student motivation.
Tone: Analytical, critical, and concrete. Avoid generic advice.
You could use this directly by attaching a rubric, assessment prompt, and “submission”, or modifying it to your own situation.
Here is a section of the results it gave, along with the “thinking” section expanded to see the process of the generated answer:
(Copilot gave me an enormous amount of feedback, as expected because the rubric included a lot of generic language.)
Rethinking “Higher-Order Thinking” in an AI-Rich Environment
Frameworks like Bloom’s Taxonomy remain useful, but AI complicates the assumption that higher-order tasks are automatically more resistant to outsourcing. AI can analyze, evaluate, and even create convincing responses if prompts are static and unconstrained.
What remains more difficult to outsource is judgment. Assignments that require students to choose among approaches, justify those choices, identify uncertainty, or explain when a method would fail tend to surface understanding more reliably than tasks that simply ask for analysis or synthesis. When reviewing AI-generated responses, a helpful question is: What would a human need to know to trust this answer? Designing assessments around that question shifts the focus from output to accountability.
Instructors can strengthen authenticity by introducing under specified scenarios, realistic limitations, or prompts that require students to articulate how they would evaluate the reliability of their own results. These design choices don’t prevent AI use, but they make it harder to succeed without understanding when and why an answer might be wrong.
An Iterative Design Loop for Assessments and Rubrics
Using AI as an assessment design diagnostic and refinement tool can work best as an iterative process. Draft the assignment and rubric, test them with AI, analyze how success is achieved, and revise accordingly. The goal is not to reach a point where AI “fails,” but rather a point where success requires engagement with disciplinary concepts and reasoning. This mirrors quality-assurance practices in other domains: catching misalignment early, refining specifications, and retesting until the design reliably produces the intended outcome. Importantly, this loop should be finite and purposeful, not an endless escalation.
Conclusion
using AI in assessment design is not about surveillance or enforcement. It is a transparency tool. When instructors acknowledge that AI exists and design accordingly, they reduce the incentive for adversarial behavior and increase clarity around expectations. Being open with students about the role of AI (what is permitted, what responsibility cannot be delegated, and how understanding will be evaluated) helps maintain trust while preserving academic standards. The credibility of online and in-person education alike depends not on stopping students from using tools, but on ensuring that passing a course still signifies meaningful learning.
Takeaway Cheat Sheet
Think of AI as support, not a villain.
Stress‑test early: run the rubric through a model for verification before you hand it to students.
For centuries, knowledge and access to education was restricted to just a few. In today’s’ world, almost anybody can access information through the web and more recently through AI tools. However, it is important to recognize that these tools, while offering expansive access to content of varied nature, also pose challenges. Generative AI has fundamentally changed how students interact with assignments, but it has also given instructors a powerful new lens for examining their own assessment design. Rather than treating AI solely as a threat to academic integrity, we can use it as a diagnostic tool – one that quickly reveals whether our assignments and rubrics are actually measuring what we think they are. If an AI can complete an assignment, and meet the stated criteria for success without engaging course-specific learning, is it really a student problem, or a signal to modify the design?
A small shift in perspective from “they’re using this to cheat” to “how can this help me prevent cheating” is especially important in online and hybrid environments, where traditional academic integrity controls like proctored exams are either unavailable or undesirable. Instead of trying to outmaneuver AI or police its use, instructors can ask a more productive question: What does success on this assignment actually require?
Why AI Is a Helpful Design Tool
AI can function as an unusually honest “devil’s advocate.” It doesn’t get tired, anxious, or confused about instructions, and it excels at finding the most efficient path to meeting stated requirements. When an instructor gives an AI model an assignment prompt and a rubric, the resulting output can expose whether the rubric rewards deep engagement or simply fluent compliance.
If an AI can generate a response that appears to meet expectations without referencing key course concepts, grappling with assumptions, or making meaningful decisions, then students can likely do the same. In this way, AI acts less like a cheating student and more like a mirror held up to our assessment design.
An example using Copilot:
Stress-Testing Assignments Before Students Ever See Them
One practical workflow to test the resilience of your assignments is to run them through AI before they are deployed. Provide the model with the prompt and the rubric (nothing else) and ask it to produce a strong submission. Then evaluate that response using your own grading criteria.
The point is not to judge whether the AI’s answer is “good,” but to analyze why it succeeds in meeting the set requirements easily and flawlessly (at first sight). If the response earns high marks through generic explanations, surface-level analysis, or broadly applicable reasoning, that’s evidence that the assessment may not be tightly aligned with course learning outcomes, focus on deeper thinking and analysis, or elicit students’ own creativity . This kind of stress-testing takes minutes, and often surfaces issues that would otherwise only become visible after grading a full cohort.
Assignment: Conceptual Design and Analysis of a Chemical Reactor
You are tasked with the preliminary design and analysis of a chemical reactor for the production of a commodity chemical of your choice (e.g., ammonia, methanol, ethylene oxide, sulfuric acid, or another well-established industrial product).
Your analysis should address the following:
Process Overview
Briefly describe the selected chemical process and its industrial relevance.
Identify the primary reaction(s) involved and classify the reaction type(s) (e.g., exothermic/endothermic, reversible/irreversible, catalytic/non-catalytic).
Reactor Selection
Propose an appropriate reactor type (e.g., CSTR, PFR, batch, packed bed).
Justify your selection based on reaction kinetics, heat transfer considerations, conversion goals, and operational constraints.
Operating Conditions
Discuss key operating variables such as temperature, pressure, residence time, and feed composition.
Explain how these variables influence conversion, selectivity, and safety.
Engineering Trade-Offs
Identify at least two major design trade-offs (e.g., conversion vs. selectivity, energy efficiency vs. safety, capital cost vs. operating cost).
Explain how an engineer might balance these trade-offs in practice.
Limitations and Assumptions
Clearly state any simplifying assumptions made in your analysis.
Discuss the limitations of your proposed design at this preliminary stage.
Your response should demonstrate clear engineering reasoning rather than detailed numerical calculations. Where appropriate, qualitative trends, simplified relationships, or order-of-magnitude reasoning may be used.
Length: ~1,000–1,200 words References: Not required, but accepted if used appropriately
The Rubric (Click to reveal)
Criterion
Excellent (A)
Good (B)
Satisfactory (C)
Unsatisfactory (D/F)
Understanding of Chemical Engineering Principles
Demonstrates strong understanding of reaction engineering concepts and correctly applies them to the chosen process
Demonstrates general understanding with minor conceptual gaps
Shows basic familiarity but with notable misunderstandings or oversimplifications
Demonstrates weak or incorrect understanding of core concepts
Reactor Selection & Justification
Reactor choice is well-justified using multiple relevant criteria (kinetics, heat transfer, safety, operability)
Reactor choice is reasonable but justification lacks depth or completeness
Reactor choice is weakly justified or based on limited reasoning
Reactor choice is inappropriate or unjustified
Analysis of Operating Conditions
Clearly explains how operating variables affect performance, safety, and efficiency
Explains effects of variables with minor omissions or inaccuracies
Provides limited or superficial discussion of operating conditions
Fails to meaningfully analyze operating variables
Engineering Trade-Offs
Insightfully identifies and explains realistic trade-offs, demonstrating engineering judgment
Identifies trade-offs but discussion lacks nuance or integration
Trade-offs are mentioned but poorly explained or generic
Trade-offs are absent or incorrect
Assumptions & Limitations
Assumptions are clearly stated and critically evaluated
Assumptions are stated but not fully examined
Assumptions are implicit or weakly articulated
Assumptions are missing or inappropriate
Clarity & Organization
Response is well-structured, clear, and professional
Generally clear with minor organizational issues
Organization or clarity interferes with understanding
Poorly organized or difficult to follow
Identifying Gaps in What We’re Measuring
AI performs particularly well on tasks that rely on recognition, pattern matching, and general world knowledge. This means it can easily succeed on assessments that emphasize recall, procedural execution, or elimination of obviously wrong answers. When that happens, the assessment may be measuring familiarity rather than understanding.
Revising these tasks does not require making them longer or more complex. Instead, instructors can focus on higher-order thinking and metacognition, for example requiring students to articulate why a particular approach applies, what assumptions are being made, or how results should be interpreted. These shifts move the assessment away from answer production and toward critical and disciplinary thinking – without assuming that AI use can or should be eliminated. The point of identifying the gaps can also help you revisit the structure of the assignment to determine how each of its elements (purpose, instructions/task/prompt, and criteria for success) are cohesively connected to strengthen the assignment.
In the second part of this blog, I take the same task above, and work with the AI to refine a rubric.
Many educators are grappling with questions about AI detection. Yet, AI detection tools are unreliable, biased, and distressing. False positives can harm students’ academic standing and well-being, with marginalized groups often disproportionately affected, while detectors still miss significant portions of AI-generated text (Lurye, 2025; Encouraging Academic Integrity, 2025; Hirsch, 2024). And detection tools assume students simply copy and paste AI outputs, when in reality many use these tools more fluidly–taking suggestions, rewriting, or iterating through prompts–making their work indistinguishable from original writing. As one student noted, “it’s very easy to use AI to do the lion’s share of the thinking while still submitting work that looks like your own…” (Terry, 2023).
What Students Want
Most students believe institutions should address academic integrity concerns related to generative AI, but they largely prefer proactive and educational approaches over punitive measures. A significant number of students want clear rules about when and how AI tools can be used, as well as a voice in shaping them (Flaherty, 2025).
From Policing to Partnership
Given the inherent risks of detection and bans — tools that can unfairly penalize students and policies that do little to promote ethical use — the better path forward is not more surveillance, but more collaboration. Faculty-written policies risk missing the mark if they ignore how students actually engage with AI. Instead of policing AI through punitive measures, faculty can create space for students to help define appropriate guidelines. Policies crafted together shift the dynamic from rules imposed to standards co-owned, building trust and relevance.
Why Co-Creation Works
Self-Determination Theory suggests that students are more likely to internalize and adhere to guidelines when they have a hand in creating them. Involving students in developing AI usage policies communicates that their perspectives are valued and supports their need for autonomy, turning compliance into genuine commitment. Co-created rules feel less like authoritarian decrees and more like shared standards, which in turn fosters ownership, clarity, and consistency in how those policies are understood and followed (Guay, 2022; Kuo et al., 2025).
Practical Approaches to Co-Create AI Policies
Research makes it clear: students are more likely to respect and follow policies they help shape. But theory alone won’t change your syllabus. The real shift happens when faculty move from principle to practice. The good news? There are straightforward, adaptable activities you can use right now to bring students into the conversation and co-create meaningful AI usage policies. For best results, implement these activities within the first week or two of the term, or before your first major assignment.
Document-Based Collaboration
1. Shared Policy Google Doc
A structured Google Doc provides policy headings (Assessment, Collaboration, Academic Integrity). Students co-edit the text under each section, adding suggestions in comments. As comments are resolved, the document evolves into a finalized class-wide AI usage policy.
Students use a wiki page to respond to realistic AI-use scenarios (e.g., “AI writing feedback on essays”). Small groups draft responses for each scenario, and peers edit for consistency. Over time, pages become a collective guide to what counts as acceptable AI use, directly forming a policy.
Tool: Canvas Wiki Pages (or equivalent LMS wiki feature)
3. Crowd-Sourced Glossary
Students collaboratively define AI-related terms and practices in a shared glossary tool (wiki or Google Doc). Each entry includes “permitted uses” and “restricted uses.” The glossary doubles as both a vocabulary aid and a concrete class AI policy.
Using a template, students co-author a charter with structured sections: Purpose, Guidelines, Responsibilities, and Consequences. Each section is drafted collaboratively, with rotating editors refining language. The final product is a polished class AI usage charter.
Instructor seeds a discussion with prompts for different policy areas. Students propose clauses and debate wording in threads. A moderator (instructor or rotating student role) synthesizes the top ideas into a consensus policy posted back to the group.
Tool: Canvas Discussions
6. Draft & Vote Forum
The instructor posts draft clauses in a forum. Students propose alternatives as replies. A class-wide vote (via Canvas poll or Google Form) determines the preferred wording. The winning clauses are compiled into the final AI policy.\
Students write sequential short blog posts on a shared course blog. Each post revises or critiques the prior entry, building momentum toward consensus. The chain of posts is later synthesized into a cohesive AI usage policy.
On a Miro board, students build a shared mind map with branches like “Learning Support,” “Integrity,” and “Assessment.” They attach notes or examples under each branch. The class then translates the map’s structure into a written, shared policy document.
An instructor uploads an external AI policy (e.g., from a university or journal) into Perusall. Students highlight passages and comment on what they agree with or want to adapt. Annotations are collected and distilled into a tailored class policy.
Students record short reflections (video or audio) on what should or shouldn’t be in the AI policy. Using Kaltura in Canvas, Microsoft Teams, or Canvas Discussions with media replies, they share contributions and respond to peers. The instructor (or a student group) compiles clips and/or transcripts into a single artifact. This collective media product is then distilled into draft clauses for the shared AI usage policy.
The instructor creates a Qualtrics survey with items such as: “Is it acceptable to use AI to generate code for an assignment?” Students select Acceptable, Unacceptable, or Conditional and provide a brief rationale. Qualtrics automatically aggregates results into tables and charts, making consensus and disagreements easy to spot. The class then uses these visual summaries to draft clear, evidence-based clauses for the shared AI usage policy.
Each student drafts a mini-policy document and submits it to a shared folder or assignment space. Using a structured rubric, peers review at least two classmates’ drafts, either through Peerceptiv or LMS assignment tools. The strongest and most frequently endorsed ideas are integrated into a composite class policy authored by the group.
Once students have contributed through these activities, the instructor’s role is to bring the pieces together. Compiling the results, highlighting areas of consensus, and drafting a clear, shareable policy ensures that the final guidelines reflect the class’s input. Sharing this draft back with students not only closes the loop but also reinforces that their voices shaped the outcome.
Before you drop a boilerplate AI statement in your syllabus, try one of these toolkit activities. Start small–maybe a survey or a media roundtable–and see how co-writing changes the game.
References
Encouraging academic integrity – University Center for Teaching and Learning. (2025). University of Pittsburgh. https://teaching.pitt.edu/resources/encouraging-academic-integrity/
Flaherty, C. (2025, August 29). How AI is changing—not ‘killing’—college. Inside Higher Ed. https://www.insidehighered.com/news/students/academics/2025/08/29/survey-college-students-views-ai
Guay, F. (2022). Applying self-determination theory to education: Regulations types, psychological needs, and autonomy supporting behaviors. Canadian Journal of School Psychology, 37(1), 75–92. https://doi.org/10.1177/08295735211055355
Hirsch, A. (2024, December 12). AI detectors: An ethical minefield. Center for Innovative Teaching and Learning. https://citl.news.niu.edu/2024/12/12/ai-detectors-an-ethical-minefield/
Kuo, T.-S., Chen, Q. Z., Zhang, A. X., Hsieh, J., Zhu, H., & Holstein, K. (2025). PolicyCraft: Supporting collaborative and participatory policy design through case-grounded deliberation. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (pp. 1–24). Association for Computing Machinery. https://doi.org/10.1145/3706598.3713865
Lurye, S. (2025, August 7). Students have been called to the office — and even arrested — for AI surveillance false alarms. AP News. https://apnews.com/article/ai-school-surveillance-gaggle-goguardian-bark-8c531cde8f9aee0b1ef06cfce109724a
Terry, O. K. (2023, May 12). Opinion | I’m a student. You have no idea how much we’re using ChatGPT. The Chronicle of Higher Education. https://www.chronicle.com/article/im-a-student-you-have-no-idea-how-much-were-using-chatgpt
“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
Too often, online courses struggle with communication that feels slow and one-sided. Students swap ideas in discussion boards, but collaboration stops there. Integrating Microsoft Teams into Canvas changes that. It brings real-time conversation, file-sharing, and group spaces directly into the LMS–helping students connect more naturally and giving instructors new ways to guide and engage. This integration not only boosts collaboration, it also provides more opportunities for Regular and Substantive Interaction (RSI) between students and instructors—structured, faculty-initiated engagement that is required in online courses under federal guidelines.
Seamless Collaboration Across Projects and Courses
Integrating Teams into Canvas ensures that group work and peer review move beyond static discussion boards into dynamic, asynchronous interactions. Students can download the app on their mobile devices, which allows them to have more consistent and real-time access to the comments and work shared by their peers. Teams allows for:
Dedicated channels for individual projects or study groups
Tagging teammates so each member of a channel knows when they are needed
File sharing by both team members and instructors
This unified workspace helps teams stay organized, accountable, and focused on shared learning outcomes. Teams has both course-level and group-level integrations. This allows instructors flexibility in how they would like to use the app. These different levels allow Teams to be used for the entire course or just for specific group projects (or both). Regardless of the level of integration and use, instructors can see how students are collaborating and completing a task or group assignment. This gives them a space to quickly jump in if students are struggling or off track.
Enhanced Communication and Community Building
Canvas announcements and emails can feel one-sided; within Teams, conversations become two-way forums where ideas flow instantly. Notifications appear directly inside Canvas (and on mobile devices if students/instructors allow), ensuring students never miss critical updates. Meanwhile, professors can host Q&A chats without scheduling hurdles by simply creating a channel in Teams. The fluid interaction nurtures a vibrant learning community, fostering peer support and timely faculty feedback. Additionally, this allows instructors to meet their Regular and Substantive Interaction goals, nurtures a collaborative online community and directly addresses the Ecampus Essentials standard of requiring all three forms (student–student, student–instructor, student–content) of interaction and engagement in a classroom.
Easy Oversight for Seeking Solutions Courses
One of the new CoreEd (Core Education is OSU’s state-of-the-art, 21st-century-focused general education program) categories being implemented this year include the Seeking Solutions courses. These courses require students to work in interdisciplinary groups and “wrestle with complex, multifaceted problems, and evaluate potential solutions from multiple points of view” (from the Seeking Solutions OSU page). This necessitates that students complete group assignments and projects while instructors mentor and monitor these groups individually.
With a fully asynchronous OSU Ecampus course, this can be difficult. One way this can be accomplished is through Teams channels. If each group has its own Teams channel and the instructor requires that they use Teams to communicate and collaborate for their project, then instructors can use this space to share resources, mentor the students, and facilitate hard conversations.
Conclusion
Integrating Microsoft Teams into Canvas reshapes the university experience by uniting collaboration and communication within a single resource. Students benefit from real-time teamwork features and greater access to their instructors, while professors enjoy streamlined group work oversight and the ability to intervene whenever necessary. Adopting this integrated approach not only enhances the quality of instruction but also fosters a more engaged and connected learning community. For more information on how to integrate Teams into your Canvas site, read the Canvas: Create linked Teams from Canvas page.
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:
“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.
I’d like to share a recent experience highlighting the crucial role of collecting and using feedback to enhance our online course materials. As faculty course developers and instructional designers, we understand the importance of well-designed courses. However, even minor errors can diminish the quality of an otherwise outstanding online course.
A lighthouse on the Oregon coast, where student feedback and technological tools act as the guiding light. Image generated with Midjourney.
A Student’s Perspective
Recently, feedback was forwarded to me submitted by an online student enrolled in a course I had helped develop.
He praised the overall design of the courses and the instructors’ responsiveness, but he pointed out some typographic and grammatical errors that caused confusion. He mentioned issues like quiz answers not matching the questions and contradictory examples.
What stood out to me was his statement:
“These courses are well-designed and enjoyable. Their instructors are great. They deserve written material to match.”
Proactive Steps for Quality Improvement
This feedback got me thinking about how we can proactively address such concerns and ensure our course materials meet the high standards our students deserve. Here are a few ideas that might help:
Implement a Feedback Mechanism
Incentivize students to hunt for flaws. Reward sharp eyes for spotting typos and grammar slips. Bonus points could spark enthusiasm, turning proofreading into a game of linguistic detective work. For example:
Weekly Surveys: Add a question to the weekly surveys asking students to report any errors they encounter, specifying the location (e.g., page number, section, or assignment).
“Did you encounter any typographic or grammatical errors in the course materials this week? If so, please describe them here, including the specific location (e.g., page number, section, or assignment).”
Assignment Feedback: Include a text-field option for students to report errors alongside their file uploads in each assignment submission.
Utilize Technology Tools
Consider using technology tools to streamline the review process and help identify typographic, grammatical, or factual errors.
AI tools
The latest advanced AI tools can assist in identifying grammatical errors, suggesting more precise phrasing, and improving overall readability. They can also highlight potential inconsistencies or areas needing clarification, ensuring the materials are more accessible to students. They can also help format documents consistently, create summary points for complex topics, and even generate quiz questions based on the content.
(Oregon State University employees and currently enrolled students have access to the Data Protected version of Copilot. By logging in with their OSU credentials, users can use Copilot with commercial data protection, ensuring their conversations are secure and that Microsoft cannot access any customer data.)
Many powerful AI tools exist. But always verify their information for accuracy. Use them as a helper, not your only guide. AI tools complement human judgment but can’t replace it. Your oversight is essential. It ensures that AI-suggested changes align with the learning goals. It also preserves your voice and expertise.
Tools for content help
Some tools can be used to target different areas of content improvement:
Grammar and Style Checkers:
Grammarly: Checks grammar, spelling, punctuation, and style
Read Aloud: A text-to-speech extension for browsers (Chrome|Firefox )
Collaborative Editing Platforms:
Google Docs: Allows real-time collaboration and suggesting mode
Microsoft Word (with Track Changes): Enables collaborative editing
Request Targeted Assistance
If specific content requires a closer review, ask for help from other SMEs, your instructional designer, colleagues, or even students. Collaboration can provide fresh perspectives and help catch errors that might have been overlooked.
Encourage Open Communication
Foster an environment where students feel comfortable reporting errors and providing feedback. Make it clear that their input is valued and will be used to improve the course.
Embrace Constructive Criticism
It’s natural to feel defensive when receiving critical feedback (I always do!), but view it as an opportunity for potential improvement. By addressing these concerns, you can enhance the quality of your course materials and ultimately improve our students’ learning experience.
This past spring marked one year in my role as an instructional designer for Ecampus. Like many of our readers, I started conversing with AI in the early months of 2023, following OpenAI’s rollout of ChatGPT. Or as one colleague noted in recapping news of the past year, “generative AI happened.” Later, I wrote a couple of posts for this blog on AI and media literacy. A few things became clear from this work. Perhaps most significantly, in the words of research professor Ethan Mollick: “You will need to check it all.”
As the range of courses I support began to expand, so did my everyday use of LLM-powered tools. Here are some of my prompts to ChatGPT from last year, edited for clarity:
What is the total listening time of the Phish album Sigma Oasis?
Answer: 66 minutes and 57 seconds
How many lines are in the following list of special education acronyms (ranging from Section 504 – the Rehabilitation Act – to TBI – Traumatic Brain Injury)?
Answer: 27 lines
Where is the ancient city of Carthage today?
Answer: Today, Carthage is an archaeological site and historical attraction in the suburbs of the Tunisian capital, Tunis.
What is the name of the Roman equivalent of the Greek god Zeus?
Answer: Jupiter, king of the gods and the god of the sky and thunder
What’s the difference between colors D73F09 and DC4405?
Answer: In terms of appearance, … 09 will likely have a slightly darker, more orange-red hue compared to … 05, which might appear brighter. (Readers might also know these hues as variations on Beaver Orange.)
And almost every day:
Please create an (APA or MLA) citation of the following …
The answers were often on point but always in need of fact checking or another iteration of the prompt. Early LLMs were infamously prone to hallucinations. Factual errors and tendencies toward bias are still not uncommon.
As you can sense from my early prompts, I was mostly using AI as either a kind of smart calculator or an uber-encyclopedia. But in recent months, my colleagues and I here at Course Development and Training (CDT)—along with other units in the Division of Educational Ventures (DEV)—have been using AI in more creative and collaborative ways. And that’s where I want to focus this post.
The Partnership
First, some context for the work we do at DEV. Online course development is both a journey and a partnership between the instructor or faculty member and any number of support staff, from training to multimedia and beyond. Anchoring this partnership is the instructor’s working relationship with the instructional designer—an expert in online pedagogy and educational technology, but also a creative partner in developing the online or hybrid course.
Fig. 1. Collaboration anchors the story of online course development at OSU (credit: Ecampus).
Ecampus now offers more than 1,800 courses in more than 100 subjects. Every course results from a custom build that must maintain our strong reputation for quality (see fig. 1). This post is focused on that big circle in the middle—collaboration with the instructional designer. That’s where I see incredible potential for support or “augmentation” from generative AI tools.
As Yong Bakos, a senior instructor with the College of Engineering, recently reminded Faculty Forum, modern forms of this technology have been around since the 1940s, starting with the influence of programmable computers on World War II. But now, he added—in challenging faculty using AI to figure out rapid, personalized feedback for learners—”we speak the same language.”
Through continued partnership, how do we make such processes more nimble, more efficient? What does augmentation and collaboration look like when we add tools like Copilot or a custom GPT? Many instructional designers have been wrestling with these questions as of late.
“Human Guided, but AI Assisted”
Here are a few answers from educators Wesley Kinsey and Page Durham at Germanna Community College in Virginia (see fig. 2). Generative AI—also known as GAI—is a powerful tool, says Kinsey. “But the real magic happens when it is paired with a framework that ensures course quality.”
Take this line of inquiry a little farther, and one starts to wonder: How might educators track or evaluate progress toward such use cases?
Funneling Toward Augmentation
As a thought experiment, I offer the following criteria and inventory—a kind of self-assessment of my own “human guided” journey through course development with generative AI (see fig. 3).
Criteria for Augmenting Development with Generative AI
ESTABLISHED – Regular, refined practice in course development — EMERGING – Irregular and/or unrefined practice, could be improved — ENVISION – Under consideration or imagined, not yet practiced
Faculty with experience teaching online may find my suggested criteria familiar; “established, emerging, envision” is adapted from an Ecampus checklist used in course redevelopment.
Fig. 3. Self-assessment of augmenting development with generative AI (CC BY-NC-SA).
Augmentation 1: From Set Up to Intake
Broadly speaking, I’m only starting to use chatbots in kicking off a course development—to capture a bulleted summary of an intake over Zoom, for example. Or with these kinds of level-setting prompts:
Remind me, what is linear regression analysis?
What fields are important to physical hydrology?
Explain to a college professor the migration of a social annotation learning tool from LTI 1.1 to 1.3.
Augmentation 2: Course Content
In my experience, instructors are only now beginning to envision how they might propose a course or develop its learning materials and activities with support from tools like Copilot—which is increasingly adept at helping us with this kind of iterative brainstorming work. The key here will be getting comfortable with practice, engaging in sustained conversations with defined parameters, often in scenarios that build on existing content. In recent practice with building assignments, I’m finding Claude 3 Sonnet helpful—more nuanced in its responses, and because you can upload brief documents at no cost and revisit previous chats.
Fig. 4. From a “more precise” conversation on citation generation. Can you spot Copilot’s errors in applying MLA style?
Augmentation 3: Suggested Revisions
Once course content begins rolling in, I apply more established practices for augmentation. For building citations of learning materials, I’m using Copilot’s “more precise” mode for its more robust abilities to read the open web and draw on various style guides (see fig. 4). With activities, often the germ of an idea for interaction needs enlargement—a statement of purpose or more detailed instructions. Here are a few more examples from working with the School of Psychological Science, with prompts edited for brevity:
What would be the purpose of practicing rebus puzzles in a lower division course on general psychology?
Please analyze the content of the following exam study guide, excerpted in HTML. Then, suggest a two-sentence statement of purpose that should replace the phrase lorem ipsum.
How should college students think about exploring Rorschach tests with inkblots? Please suggest two prompts for reflection (see fig. 5.)
Fig. 5. From an augmented reflection activity in PSY 202H, General Psychology (credit: Juan Hu).
Augmentation 4: Discussion, Planning & Review
As with course planning, I’m not quite there yet with using generative AI to shape module templates and collect preferred settings for the building I do in Canvas. But by next year—armed perhaps with a desktop license for Copilot—I can imagine using AI to offer instructors custom templates or prompts to accelerate the design process. One more note on annotating augmentation—it’s incredibly important to let my faculty partners know—with consistent labeling—when I’m suggesting course content adapted from a conversation with AI. Most often, I’m not the subject matter expert—they are. That rule of thumb from Ethan Mollick still holds true: “You will need to check it all.”
Augmentation 5: Building & Rebuilding—More Efficiently
Finally, I look forward to exploring opportunities for more efficiently writing and revising the code behind everything we do with support from generative AI. Just imagine if the designer or instructor could ask a bot to suggest ways to strengthen module learning outcomes or update a task list, right there in Canvas.
Your Turn
With the above inventory in mind, let’s pause to reflect. To what extent are you comfortable using generative AI as a course developer? In what ways could this technology supplement new partnerships with instructional designers—or other colleagues involved in the discipline you teach? Together, how would you assess “augmentation” at each stage of the course development process?
Looking back on my own year of “human guidance with AI assistance,” I now turn more reflexively to AI for help with frontline design work—even as our team considers, for example, the ethical dimensions of asking chatbots to deliver custom graphics for illustrating weekly modules. In other stages, I’m still finding my footing in leveraging new tools, particularly during set up, refresh, and redesign. As we continue to partner with faculty, I remain open to navigating the evolving intersection of AI and course development.
(And now, for fun: Can you spot the augmentation? How much of that last sentence was crafted with support from a “creative” conversation with Copilot? Find the answer below.)
Resources, etc.
The following resources may be helpful in exploring generative AI tools, becoming more fluent with their applications, and considering their role in your teaching and learning practices.
All the buzz recently has been about Generative AI, and for good reason. These new tools are reshaping the way we learn and work. Within the many conversations about Artificial Intelligence in Higher Ed a common thread has been appearing regarding the other AI–Academic Integrity. Creating and maintaining academic integrity in online courses is a crucial part of quality online education. It ensures that learners are held to ethical standards and encourages a fair, honest, and respectful learning environment. Here are some strategies to promote academic integrity and foster a culture of ethical behavior throughout your online courses, even in the age of generative AI.
Create an Academic Integrity Plan
Having a clear academic integrity plan is essential for any course. Create an instructor-only page within your course that details a clear strategy for maintaining academic integrity. This plan might include a schedule for revising exam question banks to prevent cheating, as well as specific measures to detect and address academic dishonesty (plagiarism or proctoring software). In this guide, make note of other assignments or places in the course where academic integrity is mentioned (in the syllabus and/or particular assignments), so these pages can be easily located and updated as needed. By having a plan, you can ensure a consistent approach across the course.
Exemplify Integrity Throughout the Course
It is important to weave academic integrity into the fabric of your course. Begin by introducing the concept in your Start Here module. Provide an overview of what integrity means in your course, including specific examples of acceptable and unacceptable behavior. This sets the tone for the rest of the course and establishes clear expectations. On this page, you might:
Offer resources and educational materials on academic integrity for learners, such as guides on proper citation and paraphrasing.
Include definitions of academic dishonesty, such as plagiarism, cheating, and falsification.
Provide guidance on how learners might use generative AI within the class, including what is and is not considered acceptable.
Add scenarios or case studies that allow learners to discuss and understand academic integrity issues, specifically related to the use of generative AI.
Connect academic integrity with ethical behavior in the larger field.
Provide a place for learners to reflect on what it means for them to participate in the course in a way that maximizes their learning while maintaining academic integrity.
Throughout the course, continue to reinforce these ideas. Reminders about academic integrity can be integrated into various lessons and modules. By articulating the integrity expectations at the activity and assignment level, you provide learners with a deeper understanding of how these principles apply to their work.
Set Clear Expectations for Assignments
When designing assignments, it is important to be explicit about your expectations for academic integrity. Outline what learners should and should not do when completing the task. For instance, if you do not want them to collaborate on a particular assignment, state that clearly. Provide examples and resources to guide learners on how to properly cite sources or avoid plagiarism. Be specific with your expectations and share why you have specific policies in place. For instance, if you want to discourage the use of generative AI in particular assignments, call out the ways it can and cannot be used. As an example, you might tell learners they can use generative AI to help form an outline or check their grammar in their finished assignment, but not to generate the body text. Share the purpose behind the policy, in this case it might be something about how a writing assignment is their opportunity to synthesize their learning and cement specific course concepts. This kind of transparency shows respect for the tools and the learning process, while also clearly outlining for learners what is acceptable.
Encourage Conversations About Integrity
Creating opportunities for learners to engage in discussions about academic integrity can help solidify these concepts in their minds. You can incorporate forums or discussion boards where learners can share their thoughts and experiences related to integrity. This also gives them a chance to ask questions and seek clarification on any concerns they may have. Encourage open dialogue between instructors and learners regarding academic integrity and any related concerns. These conversations can also extend beyond the classroom, exploring how integrity applies in your field or career paths. By connecting academic integrity to real-world scenarios, you help learners understand its relevance and importance in their professional lives.
Foster a Supportive Learning Environment
A supportive learning environment can help reinforce academic integrity by making learners feel comfortable asking questions and seeking guidance. Offer resources like definitions, guides, or access to mentors who can provide additional support. When learners know they have access to help, they are more likely to adhere to integrity standards. With generative AI in the learning landscape, we will inevitably encounter more “gray areas” in academic integrity. Be honest with your learners about your concerns and your hopes. Being open to conversations can only enhance the learning experience and the integrity in your courses.
We all play a role in cultivating a culture of academic integrity in online courses. By documenting a clear plan, weaving integrity into the course content, setting clear expectations, encouraging conversations, and providing support, you can create an environment where honesty and ethical behavior are valued and upheld. This not only benefits learners during their academic journey but also helps them develop skills and values that will serve them well in their future careers.
