AI Pedagogy at Bentley

Not automatically. A “no AI” choice can be just as intentional as an AI-integrated choice when it protects the learning students need to do themselves.

• Start with the learning goal, not the tool.
• Ask what students need to know, practice, and be able to do.
• Consider how AI is already present in students’ academic, professional, and personal lives — and how that presence may shape the way they encounter your course content.
• Use AI only if it strengthens learning, feedback, practice, reflection, or transfer.
• Avoid AI where it replaces the thinking students most need to build.

Research on learning highlights the importance of prior knowledge, motivation, metacognition, purposeful practice, feedback, and opportunities to apply learning in new contexts. AI use should therefore be judged by whether it supports or disrupts those processes.

The AI Assessment Scale treats “No AI” as one legitimate assessment design choice, particularly when students need to demonstrate knowledge, understanding, or skills independently. It is not a failure to innovate.

Additional Resources: 

Assessment framework: The AI Assessment Scale

A five-level framework for deciding what role, if any, AI should play in an assessment. It begins with learning outcomes and includes “No AI” as a legitimate, non-hierarchical design choice. 

Decision-making framework: Analyzing the Implications of AI for Your Course — Stanford Teaching Commons

Helps instructors examine whether AI supports their particular course through the lenses of student learning, academic integrity, equity, and faculty workload.

Map the learning before making the AI decision.

• Map the learning before making the AI decision.
  • Start with the competencies.
    What should students know, practice, and be able to do by the end of the course?
  • Map the learning activity.
    What will students do to develop and demonstrate those competencies?
  • Use the ICAP Framework as a filter.
    Look at what students will actually do during the activity:
    • Passive: Receive information without doing anything further with it.
    • Active: Select, manipulate, identify, or rehearse information.
    • Constructive: Generate something beyond what was provided, such as an explanation, interpretation, comparison, question, or justification.
    • Interactive: Build knowledge through substantive dialogue in which contributions respond to and develop one another.
  • Keep AI out when it makes student thinking more passive, less visible, or unnecessary.
  • Bring AI in when it helps students engage more deeply, such as by requiring them to:
    • compare multiple responses;
    • identify errors or assumptions;
    • explain and justify decisions;
    • revise work using feedback;
    • test an argument or solution;
    • defend their reasoning; or
    • build upon ideas through dialogue.

• Clarify AI’s role in each activity.

  • No AI: Students must demonstrate the competency independently.
  • AI as a learning partner or scaffold: AI supports practice, feedback, questioning, or revision, while students remain responsible for generating and evaluating ideas.
  • AI as an offload tool: AI handles a bounded task that is not itself a learning goal, such as reformatting information or producing an initial transcription.
  • Make student thinking visible.
    Ask students to explain, annotate, compare, reflect, revise, verify, or defend how they reached their conclusions.

  Why ICAP matters here?

  ICAP helps faculty examine the quality of student engagement, rather than assuming that using a new technology automatically produces active learning. An AI activity may support learning when it prompts students to generate, evaluate, connect, or defend ideas. It may bypass learning when students simply request and accept an answer.

  Additional Resources: 

• ICAP Framework

• Using Backward Design to Plan Your Course (From Ohio State University) 

   

   

Students need more than a list of AI rules. They need faculty guidance that helps them understand how, when, and why AI may or may not support their learning. This means creating a course environment where students can practice responsible use, reflect on their decisions, and continue building their own judgment.

• Clarity: What AI use is allowed, limited, required, or not appropriate for this course and each assignment?
• Purpose: Why does this expectation matter for the learning students are expected to do?
• Modeling: How do people in this discipline question, verify, document, revise, or choose not to use AI?
• Practice: Where can students safely experiment with AI, compare responses, identify limitations, critique outputs, or revise their work?
• Reflection: What did AI contribute, what did the student contribute, and what decisions still required human judgment?
• Verification and accountability: How should students check AI-generated claims, sources, calculations, interpretations, or recommendations? Who remains responsible for the final work?
• Feedback: How will students know whether both their disciplinary thinking and their AI-related decision-making are improving?

• Access and alternatives: Do all students have equitable access to the required tools, appropriate technical support, and a reasonable alternative when privacy, accessibility, cost, or other concerns make AI use difficult?

  Faculty do not need to have every answer about AI. Students benefit when instructors communicate their expectations clearly, explain their reasoning, model thoughtful use, and create space to revisit those expectations as the course and technology evolve.

Additional Resources: 

• Cornell's AI Attribution Guidelines
• Communicating Course Policies and Talking with Students - UC Berkely 

Not every part of a learning task serves the same purpose. Some moments help students build foundational knowledge, some provide supported practice, and others ask students to demonstrate judgment, synthesis, or transfer.

Faculty can decide where AI belongs by identifying the thinking students need to practice at each stage.

Before consulting AI:
When foundational knowledge or an independent first attempt matters, students may need to retrieve, explain, analyze, solve, or form an initial position on their own. Actively recalling information strengthens long-term retention, while an initial attempt gives students something of their own to compare, question, and revise.

With AI support:
Students can use AI to generate alternatives, test counterarguments, seek feedback, role-play perspectives, compare approaches, identify gaps, or revise their work. These uses are most productive when students must actively evaluate and build upon the response rather than simply accept it.

Independently of AI:
Students should still have opportunities to demonstrate the knowledge and capabilities the course expects them to possess themselves. Depending on the discipline, this may include foundational knowledge, disciplinary judgment, ethical reasoning, interpretation, synthesis, oral explanation, final decisions, or reflection.

Appropriately offloaded to AI:
AI may handle parts of a task that are not central to the learning goal, such as reformatting material, reorganizing notes, producing practice examples, clarifying instructions, or completing routine transformations. What can be offloaded will vary by course: a task that is incidental in one discipline may represent essential practice in another.

A useful question is:

If AI completes this part, what thinking will the student no longer practice?

If the answer names an important course competency, keep that thinking with the student or redesign the activity so the student must examine, explain, verify, or defend the AI-supported work.

When AI is available, the final product may not be enough to show what students understand. Build in moments where students make their thinking visible through multi-step assignments.

• Process: Ask students to show drafts, checkpoints, notes, or decision points.
• Explanation: Ask students to explain why they made specific choices.
• Reflection: Ask what AI helped with, what they questioned, and what they still had to decide.
• Revision: Ask students to show how feedback shaped their next version.
• Transfer: Ask students to apply their learning to a new case, problem, or context.

For more ideas for assignment design using AI, check out the AI Pedagogy Strategy Deck

Additional Resources: 

Project Zero Thinking Routines Toolbox - Strategy Collection | Harvard Graduate School of Education

A collection of short, adaptable routines that help students observe closely, organize ideas, explain reasoning, consider different perspectives, and reflect on how their understanding is changing. Routines such as Claim–Support–Question and Connect–Extend–Challenge can be used before, during, or after AI-supported work.

Metacognitive Strategies - Practical Guide | Cornell University Center for Teaching Innovation

Provides simple activities that help students examine their own thinking, recognize gaps or errors, and identify what they still need to learn. These strategies can be incorporated through planning prompts, learning reflections, exam wrappers, and brief explanations of decisions.

Rigor comes from the cognitive work students must do: applying, explaining, revising, judging, and transferring knowledge. Tasks that require effortful generation and application can support more durable learning than tasks focused only on polished output (Bjork, 1994; Bransford & Schwartz, 1999). 

A strong assignment should make clear what students are expected to understand, how AI may or may not support the task, and where their own judgment must lead. 

• Identify the disciplinary thinking the assignment is meant to develop. 

• Ask whether AI can already produce a convincing version of the final product. 

• Add requirements that depend on course-specific concepts, cases, data, discussions, or frameworks. 

• Require students to explain decisions, trade-offs, assumptions, or evidence. 

• Be transparent about where AI is allowed, limited, or not appropriate. 

• Make the learning purpose of those AI boundaries visible to students. 

Design for the thinking you want students to practice, not just the product you want them to submit. 

Formative assessment supports learning when students receive information they can use to improve (Black & Wiliam, 1998). Metacognition also matters because students learn more effectively when they monitor their understanding and adjust their strategies (Schraw, 1998). 

When AI can help produce polished work, faculty may need more than the final submission to understand what students know. Process-visible assignments help students show how their ideas developed, where they made decisions, and how their reasoning changed over time. 

• Add checkpoints such as outlines, drafts, plans, annotations, or concept maps. 

• Ask students to explain what changed between versions and why. 

• Use decision logs to capture assumptions, trade-offs, and choices. 

• Ask students to identify what feedback they used, rejected, or revised around. 

• Include short reflection prompts about how their thinking developed. 

• Assess reasoning and revision alongside the final product. 

The final product hides the learning process so build in moments where students show how they got there. 

Retrieval practice strengthens long-term learning, and feedback is most useful when students interpret it and use it to improve their work (Roediger & Karpicke, 2006; Hattie & Timperley, 2007). 

AI can increase opportunities for practice and feedback, but students still need to do the work of attempting, interpreting, revising, and deciding. The learning happens when students retrieve, explain, compare, and improve — not when they simply accept an AI-generated answer. 

• Use AI to generate practice questions, examples, counterarguments, or feedback prompts. 

• Ask students to attempt the task before consulting AI. 

• Have students compare AI feedback with their own reasoning. 

• Require students to explain what feedback they accepted, rejected, or modified. 

• Use AI to support revision, but assess the student’s reasoning behind the revision. 

• Keep students responsible for judgment and final decisions. 

AI can create more chances to practice and revise, but students must still do the meaning-making. 

The ICAP framework suggests that learning deepens as students move from passive engagement to active, constructive, and interactive engagement (Chi & Wylie, 2014). Activities should therefore be designed so AI moves students toward explanation, critique, construction, and dialogue — not passive consumption. 

Active engagement is not just about keeping students busy. It means designing tasks where students explain, compare, critique, generate, discuss, revise, or apply ideas. AI can support engagement when it gives students something to analyze or respond to, but it can weaken engagement when students passively accept its output.  

• Ask students to critique AI reasoning rather than treat it as an authority. 

• Have students compare their own first attempt with an AI-generated response. 

• Use AI to generate cases, scenarios, counterarguments, or role-play partners. 

• Ask students to explain where AI output is strong, incomplete, generic, or misleading. 

• Design peer discussion around differences between student thinking and AI output. 

• Keep the student in the role of evaluator, reviser, decision-maker, or teacher. 

Situated learning research argues that knowledge is shaped by the activity, context, and culture in which it is used (Brown, Collins, & Duguid, 1989). Transfer research also emphasises that students need opportunities to apply knowledge flexibly across contexts, not only reproduce it in familiar forms (Bransford & Schwartz, 1999). 

Authentic assessments ask students to use course knowledge in ways that resemble the complexity of real disciplinary or professional practice. In an AI-shaped world, this means designing assessments where students apply concepts in specific contexts, make judgments, explain decisions, and adapt their thinking when conditions change. 

• Use real or realistic cases, audiences, datasets, scenarios, or professional constraints. 

• Ask students to apply course frameworks to specific contexts rather than summarise general ideas. 

• Require explanation of judgment, trade-offs, limitations, and ethical considerations. 

• Include a transfer task: ask students to apply the same concept in a new or changed context. 

• Use oral defences, scenario extensions, portfolios, or reflective memos when appropriate. 

• Assess how students reason through complexity, not only what they produce. 

Students are more likely to act on expectations when they understand the goal, the criteria, and the next step in their learning process (Hattie & Timperley, 2007). In the context of AI, clarity should not only tell students what is allowed; it should help them understand why certain uses support learning and why others may bypass it. AI literacy also involves learning to evaluate, communicate with, and use AI responsibly, which means expectations should help students build judgment, not just follow rules (Long & Magerko, 2020; UNESCO, 2024). 

• Name the level of AI use: Is AI prohibited, limited, allowed with disclosure, encouraged, or required? 

• Make it assignment-specific: A course-wide policy helps, but students need clear guidance for each major task. 

• Explain the "Why”: Tell students why AI is allowed in some parts of the work and restricted in others. 

• Give examples: Show what acceptable, questionable, and unacceptable AI use looks like in your course. 

• Clarify responsibility: Students are responsible for the accuracy, ethics, reasoning, citations, and final decisions in their work. 

• Ask for transparency: When AI use is allowed, ask students to briefly explain how they used it and what choices remained on their own. 

• Revisit expectations: Restate AI expectations before major assignments, not only in the syllabus. 

A strong AI policy does more than prevent misuse. It teaches students how to make better learning decisions when AI is available. 

Students are more likely to use expectations well when they understand the standards of the task, what quality looks like, and how to regulate their own work against those expectations (Sadler, 1989; Nicol & Macfarlane-Dick, 2006). In AI-related guidance, the goal is not just to say “allowed” or “not allowed,” but to help students understand the learning reason behind the boundary. 

• Use plain language. Avoid vague terms like “responsible use” unless you define what that means in your course. 

• Separate course policy from assignment guidance. Students need to know the general rule and the specific rule for each task. 

• Show examples. Give 2–3 examples of acceptable, questionable, and unacceptable AI use. 

• Explain the why. Connect each expectation to the learning goal: What thinking are students meant to practice here? 

• Use quick checklists. Before submission, ask students to confirm what AI use was permitted, what they used, and what remained their own. 

• Revisit expectations at the moment of use. Remind students before major assignments, not only on the first day of class. 

If students cannot explain the AI expectation back to you in their own words, the policy may not be clear enough yet. 

AI disclosure should not feel like a confession; it should function as a learning record. Research on self-regulated learning emphasizes that students learn when they monitor their process, evaluate their choices, and understand the standards they are working toward (Nicol & Macfarlane-Dick, 2006). AI literacy also involves knowing how to evaluate and use AI responsibly, not simply whether a tool was used (Long & Magerko, 2020). 

• Ask what they used AI for. Brainstorming, outlining, feedback, coding, editing, summarizing, generating examples, etc. 

• Ask where AI entered the process. Before drafting, during revision, after feedback, or near final submission? 

• Ask what they accepted, changed, or rejected. This reveals judgment, not just tool use. 

• Ask what remained their own. What decisions, interpretations, evidence, or reasoning did they produce? 

• Keep it proportional. A short note is often enough; not every assignment needs a full AI-use log. 

• Normalize transparency. Frame disclosure as part of responsible learning, not as an admission of wrongdoing. 

The most useful AI disclosure does not just answer “Did you use AI?” It helps students explain how they used judgments when AI was available. 

For Academic Integrity regarding AI at Bentley refer to the Guidelines for Responsible Use of AI 

Academic integrity is strongest when it is treated as a learning culture, not only a misconduct system. Bretag and Mahmud (2016) emphasize access, responsibility, detail, and support as core parts of exemplary academic integrity policy. In AI-era assessment, detection alone is not enough, especially because AI detectors can be unreliable and may misclassify some student writing, including non-native English writing (Liang et al., 2023). Instead, build integrity into the assignment design. 

• Use an AI-use level for each assignment. Tell students whether AI is prohibited, limited, allowed with disclosure, or expected — and explain why. 

• Require an “attempt before AI” moment. Ask students to submit an initial idea, outline, problem attempt, or response before using AI. 

• Build in process checkpoints. Use drafts, plans, decision logs, annotated notes, or short progress submissions so learning is visible over time. 

• Ask for an AI-use note when AI is allowed. Students briefly explain what they used AI for, what they changed, what they rejected, and what remained their own. 

• Anchor work to course context. Require students to use course readings, frameworks, cases, datasets, class discussions, or local examples that generic AI output cannot easily replace. 

• Include a reflection or revision memo. Ask students to explain how their thinking changed, what feedback shaped their work, and why they made final decisions. 

• Use short explanation checks. Add a brief oral defense, in-class walkthrough, or follow-up question where students explain their reasoning. 

• Assess judgment, not just polish. Include criteria for reasoning, evidence use, decision-making, revision, and transfer — not only clarity or completeness. 

• Respond to concerns through conversation first. If something seems off, ask the student to explain their process, sources, decisions, and reasoning before treating it as misconduct 

The goal is not to catch students using AI. The goal is to design learning environments where students understand what responsible work means and can show the thinking behind it. 

Responsible AI use is easier to teach when academic integrity is framed as a shared learning practice, not only a compliance issue. Research on academic integrity emphasizes clear expectations, shared responsibility, detailed guidance, and support for students before misconduct occurs (Bretag et al., 2011). AI literacy research also suggests that students need opportunities to evaluate, collaborate with, and use AI responsibly — not just be told whether it is allowed or banned (Long & Magerko, 2020). 

• Talk about AI before the first high-stakes assignment. Do not wait until misuse happens to define responsible use. 

• Name the learning purpose behind your AI boundaries. Students should know why AI is allowed in one task and limited in another. 

• Use low-stakes AI practice. Let students try AI, critique its output, identify limitations, and discuss what responsible use looks like in your field. 

• Model your own decision-making. Show students how you evaluate an AI response, question its reasoning, check its sources, or revise its output. 

• Make integrity part of assignment design. Build in checkpoints, reflections, process notes, AI-use disclosures, or revision memos. 

• Normalize transparency. Treat AI disclosure as part of learning how to work responsibly, not as an automatic sign of wrongdoing. 

• Create room for judgment. Ask students what they accepted, rejected, changed, or questioned when using AI. 

• Use concerns as teachable moments when appropriate. When expectations are unclear or misuse seems rooted in misunderstanding, clarify, reteach, and redesign before escalating.