Introduction to Learning Theories
A 150-minute flipped, socially-constructivist lesson with integrated AI literacy. Students compare behaviorism, cognitivism, constructivism and social constructivism — then critically evaluate AI-generated explanations, lesson designs and feedback, documenting when human judgement must take precedence.
Intended Learning Outcomes (ILOs / CILOs)
By the end of this lesson — and its portfolio — students will be able to:
Compare and contrast behaviorism, cognitivism, constructivism and social constructivism by identifying core assumptions, learning mechanisms and instructional implications.
Apply at least two learning theories to analyze authentic classroom scenarios and propose evidence-based instructional strategies.
Collaboratively evaluate peer-generated theory applications using a shared rubric and provide constructive, theory-grounded feedback.
Create a multimodal concept map or mini-lesson plan that constructively aligns a chosen learning theory with learning outcomes, activities and assessment.
Critically evaluate AI-generated explanations and instructional recommendations about learning theories for accuracy, completeness, bias and pedagogical fit.
Exercise independent judgement by documenting when to accept, modify or reject AI-generated content in learning-theory analysis and instructional design.
Flipped Learning requires pre-class knowledge acquisition and in-class active application. Social Constructivism requires collaborative negotiation of meaning. The AI outcomes add critical AI literacy: using AI tools critically, evaluating outputs for accuracy and bias, and deciding when human judgement must take precedence.
Two of the six outcomes (AI-ILO5, AI-ILO6) are AI-specific and are assessed across every activity via the AI Decision Log. When presenting outcomes to students, show the AI outcomes alongside the disciplinary ones so the message is explicit: evaluating AI is a graded, first-class academic skill here — not an optional extra.
Self-check: can I do this?
Tick each outcome you can already evidence. Progress is saved in your browser.
Success criteria (student-facing "I will know I have succeeded when…")
- My comparison uses at least three analytic dimensions (assumptions, mechanism, role of learner, assessment implication).
- My scenario analysis cites theory by name and links each strategy back to a mechanism.
- My peer feedback names one strength and one theory-linked improvement.
- My AI Decision Log contains at least one modify and one reject decision with a rationale.
- I can state one thing AI got wrong today and how I know.
Pre-Class Preparation Flipped component
Completed before the session — this is the knowledge base students bring into the Jigsaw groups.
Input materials
- Watch four 6–8 minute videos on behaviorism, cognitivism, constructivism and social constructivism.
- Read the assigned chapter.
- Annotate collaboratively via Perusall or Hypothesis: contribute one example and one confusion.
- How does each theory define learning?
- What is the role of the learner and the teacher?
- What instructional methods follow from each theory?
Diagnostic pre-test AI-assisted misconceptions
8-question adaptive quiz in the LMS. Feedback explains common misconceptions — e.g. "behaviorism is only about punishment" or "constructivism means no teacher guidance."
Try the in-page version below (Section 3) to see the misconception-first feedback design.
Use AI to draft distractor rationales for each quiz item: prompt "For this misconception, write a 2-sentence explanation of why a first-year student might hold it and what the correct understanding is." Instructor then verifies and edits every rationale — AI drafts, the human validates.
AI engagement task Pre-class
- Prompt ChatGPT or Claude: "Explain the difference between behaviorism and constructivism and give one classroom example for each."
- Record one strength, one limitation and one possible bias or hallucination in the AI output.
- Submit to the LMS using the AI Literacy Checklist.
Remind students that AI is a study partner, not an authority. Students should maintain independent reasoning and avoid unreflective reliance on AI suggestions.
Pre-class completion checklist
Introduction 15 minutes
Hook → misconception analysis → real-world framing → modelling AI critique.
The hook AI-generated scenario
"A teacher uses stickers to reward reading. AI claims this is constructivist because students construct meaning."
Ask students: Is the AI claim accurate? What is accurate? What is missing? What bias is present? How would you correct it?
Do not reveal the answer immediately. Let students discover that sticker rewards are a behaviorist reinforcement mechanism, and that the AI has mislabelled the theory — a category error. This 3-minute moment establishes the whole lesson's stance: AI fluency includes disagreeing with AI, on evidence.
Pre-test review with misconception analysis Mentimeter
Clarify two headline misconceptions:
- Behaviorism is not only reinforcement (and reinforcement ≠ only extrinsic rewards).
- Constructivism does not mean abandoning teacher guidance.
Teachers are theory-informed decision makers. Learning theories shape how we design instruction, assess learning and support diverse students.
Opening poll (live)
Launch before the misconception review so students see the spread of opinion in real time. Results stay anonymous, which lowers the stakes for first-year students.
Question: "The sticker-reward lesson is… (a) behaviorist (b) cognitivist (c) constructivist (d) not sure."
Follow-up: "Why did nearly half the room choose constructivist?"
MZHow to run the misconception review in Mentimeter (numbered steps)
Learning objectives served: ILO1 (accurate theory assumptions).
- Go to Mentimeter to create an interactive poll and sign in with your institutional account (free education tier available).
- Click New presentation → Add slide → choose Multiple Choice for the sticker scenario question.
- Add 2–3 further Multiple Choice slides, one per common misconception: "Behaviorism is mainly about punishment" (True/False); "Constructivism means the teacher gives no guidance" (True/False); "Cognitivism focuses on observable behaviour" (True/False).
- Optional: add a Word Cloud slide — "One word: what does 'learning' mean to you?" — to open discussion.
- Press Present and share the QR code + join code on the big screen. Students vote from phones/laptops at menti.com with the voting code.
- Reveal results live and annotate the misconceptions verbally. Save the results slide into the LMS as a record for the reflection section.
GFAlternative: run it as a Google Forms self-marking diagnostic
Learning objectives served: ILO1; feeds the pre-test → post-test comparison.
- Open Google Forms to build a self-marking quiz and select the Quiz toggle in Settings.
- Add one question per misconception. For each, add answer feedback explaining why the wrong answer is tempting.
- Enable Response receipts and link the form to a Google Sheet for misconception clustering.
- Share the form via the LMS. Review the summary chart at the start of class.
Development Activities 110 minutes
Five segments: Jigsaw → AI Critique Lab → Gallery Walk → Design Sprint → Cross-Theory Synthesis.
Segment 1 · Jigsaw Expert Groups 20 min Flipped + Social Constructivism
- Groups of 4–5. Each group becomes an expert on one theory: behaviorism, cognitivism, constructivism or social constructivism.
- Using pre-class materials, create a 3-slide summary covering: core assumptions · learning mechanism · instructional implications.
- Groups prompt AI to generate a summary of their assigned theory.
- Compare the AI summary with their own.
- Identify one inaccuracy, one omission and one potential bias.
- Record findings in the AI Decision Log.
Flipped Learning — pre-class knowledge is applied in class. Social Constructivism — groups collaboratively negotiate meaning.
"Summarise [theory] in 150 words: core assumptions, how learning happens, and two instructional implications. State your confidence level."
Segment 2 · AI Critique Lab 25 min Critical AI literacy
- Groups receive a scenario — e.g. teaching fractions, classroom management, or online discussion.
- Prompt AI: "Design a 10-minute lesson using [assigned theory]."
- Evaluate the output against a 4-criteria rubric: accuracy · completeness · bias · pedagogical fit.
- Students must modify or reject at least one AI suggestion and justify their decision.
- Document what was accepted, modified or rejected.
Maintain independent reasoning. Recognise when AI is appropriate versus when human judgement is essential. Avoid unreflective reliance on AI suggestions.
Flipped Learning active application; critical AI literacy.
AIRubric: evaluating an AI-generated lesson (use in the Critique Lab)
Learning objectives served: AI-ILO5, AI-ILO6, ILO2.
| Criterion | What to ask | Typical AI failure |
|---|---|---|
| Accuracy | Are the theory claims correct? | Mislabels strategies (rewards called "constructivist"). |
| Completeness | What has been left out? | Omits assessment or differentiation; ignores prior knowledge. |
| Bias | Whose context is assumed? | Assumes well-resourced, Western, neurotypical, English-first classrooms. |
| Pedagogical fit | Does it match the stated theory and the scenario? | Generic activity that could fit any theory. |
Segment 3 · Scenario Application Gallery Walk 25 min Think-pair-share + Mini-lecture
- Groups rotate to four stations, each with a classroom scenario. At each station, apply a different theory.
- Use AI as a consultant: ask for one strategy, then critique it.
- Add sticky notes with evidence-based strategies. Use Padlet for a digital gallery.
Think-pair-share · interactive lecture mini-inputs · animated visualisations of key concepts.
Students interrogate AI-generated strategies for accuracy, completeness and bias. They refine or challenge AI outputs rather than copying them.
PDSet up the digital gallery walk in Padlet — numbered steps
Learning objectives served: ILO2, ILO3; supports Social Constructivism's collaborative knowledge-building.
- Go to Padlet to create a collaborative wall and sign in (education accounts get a limited number of free walls).
- Click Make a Padlet → choose the Wall or Grid format (Grid keeps a tidy 4-column station layout).
- Create four sections/columns, one per station scenario: e.g. Station 1 — Teaching fractions, Station 2 — Classroom management, Station 3 — Online discussion, Station 4 — Mixed-ability group work.
- In the settings panel, enable Comments (for peer critique) and set Posting → Display name to "Group number" so posts are traceable but not personal.
- Set the privacy to Secret link, then share the link or QR code via the LMS and on the projector.
- Ask each group to post: (1) the strategy, (2) the theory it maps to, (3) one AI suggestion they challenged. Use the colour/tag feature to code by theory.
- At the end, use Padlet's Export → CSV to archive the wall into the course portfolio.
KHGamified check: "Which theory fits this scenario?" with Kahoot
Learning objectives served: ILO1, ILO2.
- Go to Kahoot to build a scenario-matching quiz and create a free teacher account.
- For each scenario, write a question with four options — one per theory — and set the correct answer plus a 20-second time limit.
- In the question editor, add a short explanation that appears after each answer to reinforce the reasoning (not just the answer).
- Launch as Host → Classic and project the game PIN; students join at kahoot.it with the game PIN.
- After the game, download the report — it shows which scenarios the class found hardest and becomes your misconception data for the closure post-test.
Segment 4 · Mini-Lesson Design Sprint 30 min AI-assisted design
- Groups create a multimodal concept map or mini-lesson plan aligning a chosen theory with outcomes, activities and assessment.
- Use AI to brainstorm ideas, then critique and revise. Include the AI Decision Log.
- Peer feedback using a shared rubric. Instructor circulates and provides scaffolding.
Provide a template, a worked example and sentence starters for students who need them, then withdraw support.
Collaborative AI-assisted problem-solving. Teams use AI to brainstorm solutions, then critique AI's contributions and document modifications.
Sentence starters for theory-grounded peer feedback
- "Your activity aligns with [theory] because it relies on [mechanism]…"
- "This assessment would better evidence [outcome] if it…"
- "One thing AI suggested that we rejected was…, because…"
- "A strength of your design is…; the next step could be…"
JBBuild the multimodal concept map in Jamboard / FigJam — numbered steps
Learning objectives served: ILO4; multimodal design criterion in the portfolio rubric.
- Open Google Jamboard to start a collaborative whiteboard (or FigJam for a richer template library) and sign in with your institutional account.
- Click the + to create a new jam; rename it Group [n] — [Theory] alignment map.
- Use the sticky note tool to create four colour-coded clusters, one per alignment element: Theory · Outcome · Activity · Assessment.
- Use the pen/draw tool to connect the clusters with arrows and label each arrow with the mechanism (e.g. "reinforcement", "scaffolding").
- Click Share → set access to "Anyone with the link can edit" and paste the link into your group's Padlet post so the instructor can circulate digitally.
- Use File → Save as PDF (or FigJam's export) to attach the artefact to the portfolio in the LMS.
Segment 5 · Cross-Theory Synthesis 10 min Interactive lecture
- Compare theories on dimensions: learner role · teacher role · assessment.
- Use animated visualisation, digital flashcards and a gamified poll: "Which theory fits this scenario?"
Ask AI to generate a comparison table. Students identify one oversimplification or missing nuance in the AI-generated table. (Common finds: treating the four theories as a strict linear progression, or omitting that most real classrooms blend theories.)
Interactive flashcards — tap to flip
Drag-and-drop: sort the concepts into the right theory
Tap a concept to select it, then tap a theory box — or drag it if you're on a desktop.
Synthesis & Closure 25 minutes
Post-test → peer-teaching summaries → AI reflection discussion → preview.
Post-test 5 questions
Five questions aligned with the initial misconceptions, so growth is measurable against the pre-test. Compare pre/post at class level and publish the shift in the next session.
Peer-teaching summaries
Each group shares one key insight and one AI critique. Keep it to 45 seconds per group to fit ~20 groups — use a visible timer.
AI reflection discussion Whole class
Prompt: "How did AI support or hinder your learning today? When did you need to override AI suggestions?"
Students share instances where AI was helpful versus misleading. Capture these on a two-column board (physical or Padlet) headed AI helped / AI misled.
"One AI suggestion I rejected and why." Collect physically or via the digital form below. This is the primary formative evidence for AI-ILO6.
Next session will focus on applying learning theories to instructional design.
Digital exit ticket (saved in your browser, then submit to the LMS)
GFThe exit ticket as a Google Form — numbered steps
Learning objectives served: AI-ILO6; feeds instructor reflection in Section 12.
- Open Google Forms to create the exit-ticket form and title it "Exit ticket — one AI suggestion I rejected."
- Add a Short answer for group/student ID, then a Paragraph field for the rejected suggestion + reason.
- Add a Multiple choice: "How certain are you that rejecting it was correct?" (Very / Fairly / Unsure) to gauge confidence calibration.
- Turn on Collect email addresses if you need per-student attribution; turn it off for anonymous honesty.
- Link responses to a Google Sheet, then group the "rejection reasons" into themes for the next session's calibration discussion.
Assessment Methods AI-integrated
Formative in-class evidence, a summative portfolio, and explicit requirements for critical AI engagement.
Formative assessment (during class)
- Pedagogy-aligned observations: group collaboration, use of pre-class knowledge, quality of peer feedback.
- Real-time polling and exit tickets. Example exit ticket: "One AI suggestion I rejected and why."
- Peer-assessment checklists/rubrics used during the Mini-Lesson Design Sprint.
- AI-supported formative checks: students submit AI-generated responses alongside their own analyses for comparison.
Instructor reviews critical engagement with AI outputs using the AI Decision Log — not the AI output itself. What is graded is the quality of the student's critique.
Summative assessment
Project portfolio: mini-lesson plan + concept map + AI Decision Log + critical reflection.
Rubric criteria
- Theoretical accuracy and depth — ILO1, ILO2
- Application to authentic scenario — ILO2
- Collaboration and peer feedback quality — ILO3
- Multimodal design and constructive alignment — ILO4
- Critical AI engagement — AI-ILO5, AI-ILO6
AI-Integrated Assessment Requirements
- AI use is explicitly allowed with full transparency.
- Students must evaluate the accuracy or limitations of AI outputs used in their work.
- Students must document what AI suggestions were used, modified or rejected.
- Students must explain how they modified or rejected AI suggestions.
- Students must articulate where and why human judgement took precedence over AI recommendations.
"Critical AI Engagement" rubric 4 levels
| Criterion | Exemplary | Proficient | Developing | Insufficient |
|---|---|---|---|---|
| Evaluates accuracy & limitations of AI (AI-ILO5) | Identifies subtle inaccuracies, omissions and bias; explains why AI errs. | Identifies clear inaccuracies and one limitation with a sound reason. | Notes something is wrong but reasoning is vague or partly incorrect. | Accepts AI output uncritically or states "it seems fine". |
| Documents decision-making (AI-ILO6) | Log is complete, specific, and shows a decision for every AI contribution. | Log records accept/modify/reject for most AI contributions. | Log is partial or records decisions without detail. | No log, or log is a copy of AI output. |
| Explains modifications / rejections | Justifications are theory-grounded and weigh pedagogical trade-offs. | Justifications are clear and theory-linked. | Justifications are given but generic. | Changes made without justification. |
| Articulates where human judgement took precedence | Names a specific teaching decision where only human judgement would do, and defends it. | Names a case where human judgement mattered and explains why. | Mentions human judgement generally, without a concrete case. | Treats AI as the final authority. |
Interactive: build your AI Decision Log
Complete at least one modify and one reject row across the lesson. Entries save in your browser — export them into your portfolio.
| AI suggestion | Decision | Rationale (theory-grounded) | Where human judgement took precedence |
|---|
AI-Supported Feedback & Learning Support Human-in-the-loop
AI generates a first pass; the instructor validates; the student interrogates.
The feedback loop Clear process
- Instructor reviews and adjusts AI-generated feedback before release.
- Students interrogate AI feedback: "Do you agree with the AI's assessment? What might it have missed?"
- Students override AI feedback where appropriate, with documented rationale.
Ensures AI serves as a supplement to, not a replacement for, human pedagogical judgement.
Instructor and students evaluate AI feedback quality together using sample work. In a 90-student cohort, run these as a whole-class projected exercise using 2–3 anonymised drafts, then have groups apply the same standard to their own drafts.
Tools in the loop
- ChatGPT / Claude — draft-level feedback and explanation.
- Gradescope — rubric-anchored marking support for the portfolio.
- Turnitin Revision Assistant — formative feedback on academic writing.
Student prompt for interrogating feedback
"Here is AI feedback on my draft: [paste]. Here is the rubric: [paste]. Which rubric criteria does this feedback address, which does it ignore, and where might it be wrong? Then tell me how I would defend my design against it."
GRSet up rubric-anchored feedback in Gradescope — numbered steps
Learning objectives served: ILO1–ILO4 plus the Critical AI Engagement criterion.
- Sign in at Gradescope to build an assignment and rubric (free for instructors; students join with an entry code).
- Create the Assignment, then under Rubric add one item per criterion from this lesson's rubric — including "Critical AI Engagement" as its own item with the four levels.
- Enable AI-assisted grading and upload 2–3 previously marked exemplars so the tool inferences your standard.
- Let AI pre-suggest rubric-item scores on student submissions, then review and adjust every submission yourself before releasing grades.
- Release feedback in batches and post an announcement asking students to respond to the "interrogate the feedback" prompt.
TRUse Turnitin Revision Assistant for draft feedback — numbered steps
Learning objectives served: AI-ILO5, AI-ILO6 (written articulation).
- Access Turnitin Revision Assistant through your institution's Turnitin licence and create a draft assignment.
- Import the critical reflection task so the tool's prompts align with your rubric language.
- Ask students to submit a draft of the reflection (not the final portfolio).
- In class, project 2–3 anonymous drafts and run the calibration session: does the AI's comment match your own judgement? Where does it miss?
- Require students to paste the AI feedback into their AI Decision Log and record one accept, one modify, one reject.
Feedback self-check
Constructive Alignment Matrix
Every outcome is matched to a teaching activity, an assessment method, a pedagogy link and an AI integration link.
| Learning Outcome | Teaching Activity | Assessment Method | Pedagogy Link | AI Integration Link |
|---|---|---|---|---|
| ILO1 Analyze theories | Jigsaw Expert Groups; Cross-Theory Synthesis | Pre-test, post-test, portfolio theoretical accuracy | Flipped Learning: pre-class content, in-class analysis | AI-generated summaries critiqued for accuracy and omission |
| ILO2 Apply theories | AI Critique Lab; Scenario Gallery Walk | Mini-lesson plan; scenario analysis | Flipped Learning: active application | AI used as consultant; students modify or reject suggestions |
| ILO3 Evaluate peer work | Peer feedback in Design Sprint | Peer-assessment rubric | Social Constructivism: collaborative critique | AI Decision Log reviewed for critical engagement |
| ILO4 Create aligned design | Mini-Lesson Design Sprint | Project portfolio: concept map / mini-lesson | Flipped + Social Constructivism | AI brainstorming critiqued and revised |
| AI-ILO5 Evaluate AI outputs | AI Critique Lab; AI reflection | AI Decision Log; critical reflection | Critical AI literacy | Evaluate accuracy, bias, limitations |
| AI-ILO6 Independent judgement | All AI tasks | AI Decision Log; rubric | Responsible AI use | Document accept/modify/reject; human judgement |
External Tool Integration
Step-by-step setup for every platform referenced in this lesson. Each card states its purpose and the outcomes it serves.
Expand only the tool you need right now. Each entry is self-contained. Test every tool with a 2-minute dry run before class — with 90 students joining at once, a broken link costs real teaching time.
CSChatGPT / Claude — AI explanation & design partner
Learning objectives served: AI-ILO5, AI-ILO6; supports ILO1 and ILO4.
- Open ChatGPT to run the AI comparison prompt or Claude to run the same tasks. Use a free account; institutional accounts may offer better data privacy.
- Run the pre-class prompt: "Explain the difference between behaviorism and constructivism and give one classroom example for each."
- Run the design prompt in the Critique Lab: "Design a 10-minute lesson using [assigned theory] for [scenario]."
- Run the synthesis prompt: "Make a comparison table of behaviourism, cognitivism, constructivism and social constructivism across learner role, teacher role and assessment."
- For every output, complete one AI Decision Log row: what was suggested, accept/modify/reject, and the rationale.
- Never paste identifiable student data, and check your institution's AI-use policy before sharing any course material.
PEPerusall / Hypothesis — collaborative social annotation
Learning objectives served: ILO1; Social Constructivism's collaborative knowledge-building.
- Create the reading assignment in Perusall to set up social annotation, or install the Hypothesis browser extension for open annotation.
- Upload the chapter PDF or paste the article URL; set the annotation deadline to 24 hours before class.
- Add two required prompts in the instructions: (1) post one worked example of a theory in action; (2) post one confusion.
- Enable automatic scoring if you want participation credit; otherwise mark manually from the dashboard.
- Export the top-voted confusions and open the lesson by addressing the three most common ones.
EDEdpuzzle — video with embedded questions
Learning objectives served: ILO1.
- Sign in at Edpuzzle to add questions to a video and create a class.
- Search for the four theory videos or upload your own; choose Edit → Questions.
- Insert open-ended or multiple-choice questions at 2–3 points per video, focusing on the theory's core assumption and mechanism.
- Turn on prevent skipping so students actually watch, and set a due date before class.
- Review the per-question analytics before class — the most-missed question becomes your opening misconception.
MZMentimeter — live polling & word clouds
Learning objectives served: ILO1, ILO2.
- Create the deck at Mentimeter to build live polls (steps in Section 3 above).
- Add the sticker-scenario vote, three True/False misconception slides, and one word cloud.
- Present, share the join code, and reveal results live.
- Export the results to PDF and archive them in the LMS for the reflection data set.
KHKahoot — gamified scenario-matching quiz
Learning objectives served: ILO1, ILO2.
- Build the quiz at Kahoot to create a scenario-matching game (full steps in Segment 3).
- Write one question per scenario with the four theories as options and an explanation after each answer.
- Host in Classic mode; students join at kahoot.it using the game PIN.
- Download the report and reuse the hardest questions in the closure post-test.
PDPadlet — digital gallery walk & knowledge wall
Learning objectives served: ILO2, ILO3.
- Create the wall at Padlet to build a collaborative gallery (full steps in Segment 3).
- Four station columns, comments enabled, secret link shared via LMS + QR code.
- Each post shows strategy + theory + the AI suggestion challenged.
- Export to CSV at the end for the portfolio archive.
JBJamboard / FigJam — collaborative concept mapping
Learning objectives served: ILO4.
- Open Google Jamboard to start a whiteboard or FigJam for template-rich boards (full steps in Segment 4).
- Four colour-coded sticky clusters: Theory · Outcome · Activity · Assessment.
- Draw labelled arrows showing the mechanism connecting each cluster.
- Set link sharing to "can edit" and export to PDF for the portfolio.
GDGoogle Docs / Drive — collaborative drafting & submission
Learning objectives served: ILO3, ILO4, AI-ILO6.
- Create a template in Google Docs to share the mini-lesson template and set it to "Make a copy for each group".
- Include the AI Decision Log table, the alignment map headings, and the sentence starters as prompts.
- Require groups to use suggesting mode for peer feedback so contributions are attributable.
- Check File → Version history to verify every member contributed.
- Collect final portfolios through the LMS, linking the Doc and the exported concept map.
GFGoogle Forms — diagnostic quizzes & exit tickets
Learning objectives served: AI-ILO6; misconception tracking for ILO1.
- Build self-marking quizzes at Google Forms to create a quiz with answer feedback.
- Enable the Quiz toggle and add explanation feedback per answer.
- Link to a Sheet and use pivot charts to cluster misconception themes.
- Compare pre-test and post-test summaries side by side when reporting growth.
Required Resources & Technology
Everything the session depends on, grouped by function.
LMS
Blackboard, Moodle or Canvas — hosting pre-test, AI Literacy Checklist, portfolio submission and archived poll data.
Flipped Learning platforms
- Perusall or Hypothesis
- Edpuzzle
- Mentimeter
Social Constructivist platforms
- Padlet
- Google Docs
- Jamboard
AI tools & platforms
- ChatGPT or Claude for AI interaction tasks.
- AI-powered feedback tools such as Gradescope or Turnitin Revision Assistant.
- AI literacy resources such as AI bias analysis tools.
Physical / digital materials
- Theory summary sheets
- Scenario cards
- Rubric
- AI Decision Log template
- Concept map template
- Sticky notes
- Tablets / laptops
Differentiation & Inclusivity
Multiple entry points, tiered challenge, and AI used deliberately as an access tool.
Pedagogy-tailored supports
- Multiple entry points via videos, readings and collaborative annotation.
- Peer mentoring.
- Tiered scenarios for different readiness levels.
Extension activities
- Advanced learners design a cross-theory integrated lesson.
- Lead an AI critique for the class.
- Explore AI bias in depth — e.g. analyse how AI describes learners from different cultural contexts.
AI-supported differentiation Access + stretch
- AI provides simplified explanations and step-by-step guidance for struggling learners.
- Advanced learners use AI to explore complex extensions of the topic.
- Accessibility accommodations through AI: AI-generated captions, text-to-speech, language translation.
- Clear guidance on AI use that accommodates varying levels of AI familiarity.
Pair AI simplification with the expectation that students still critique it. A simplified explanation can still contain an error — the access support and the critical habit are trained together, not in competition.
Accessibility accommodations
- Captioned videos and full transcripts.
- Tactile concept map options (physical sticky notes, raised-line boards).
- Flexible grouping — mixed-ability and readiness-matched, by task.
Reflection & Improvement
What we measure to know whether the flipped, socially-constructed, AI-integrated design actually worked.
Pedagogy-specific success indicators
- Pre-class completion.
- Quality of in-class application.
- Group collaboration efficacy.
Student feedback
- Flipped Learning self-evaluation.
- Social Constructivist collaboration survey.
AI-specific reflection Three lenses
- Student feedback: "Did AI tools enhance or distract from your learning? Were you able to maintain your own thinking?"
- Instructor reflection: "Did AI support achieve intended goals? Where did students struggle with AI criticality?"
- Data on critical engagement: instances where AI was overridden; accuracy of AI versus student evaluations.
Modification strategies
- Revise case studies.
- Adjust AI prompts.
- Add more calibration on AI feedback.
- Strengthen scaffolding for AI literacy.
The AI Decision Logs exported in Section 6 and the exit tickets from Section 5 are the primary evidence base. Count how often students overrode AI, and sample why — that gives you the calibration focus for next iteration.
Materials
Existing resources, plus the interactive tools built for this lesson.
Existing materials
Non-existing materials
Interactive tools
- Quiz: Learning Theory Scenarios — Flipped Learning — adaptive feedback, misconception analysis. (Use the in-page pre-test below.)
- Flashcard: Learning Theory Key Concepts (Flipped Learning) — interactive drag-and-drop. (Section 4, Segment 5.)
- AITool: AI Learning Theory Critique — Critical Engagement Focus — students evaluate AI-generated lesson plans for accuracy, bias and pedagogical fit.
- AITool: AI Decision Log Template — Documentation Purpose — students record accept/modify/reject decisions. (Section 6.)
Non-interactive activities
Pre-class: students watch videos and complete collaborative annotation. In-class: Jigsaw expert groups synthesize theory, then apply it to scenarios. Use AI to generate summaries and lesson ideas, then critique and revise.
Groups negotiate meaning by comparing AI-generated explanations with course materials. They identify inaccuracies and biases, then co-construct a corrected explanation. Instructor facilitates discussion on when AI was helpful and when human expertise was essential.
Students prompt AI to design a lesson using behaviorism. They evaluate the output for accuracy, completeness, bias and pedagogical fit. They modify one element and reject another, documenting their rationale. The class discusses where human judgement took precedence.
In-page adaptive quiz — Learning Theory Scenarios 8 questions
Answer all eight, then press Check answers for misconception-first feedback. Use this as the flipped pre-test or the closure post-test.