Foundations of Education · Lesson 3
Introduction to Learning Theories
This 150-minute flipped session introduces first-year undergraduates to five core learning theories - behaviourism, cognitivism, constructivism, social constructivism and humanism. Students co-construct comparative understanding through a jigsaw, peer critique and AI-assisted problem-solving, while treating every AI output as a claim to be interrogated rather than an answer to be trusted.
Section 1
Intended Learning Outcomes
Every outcome below is engineered so that AI integration is explicit: each ILO names both the cognitive demand and the critical, transparent way AI is used to support (and be questioned by) the learner.
| ILO | Description | 🤖 AI Integration |
|---|---|---|
| ILO1 | Analyse core learning theories - behaviourism, cognitivism, constructivism, social constructivism and humanism - using key concepts, evidence and theoretical assumptions. (Bloom: Analyse) | Students prompt an AI for a one-paragraph summary of each theory, then audit the summary sentence by sentence for accuracy, omissions and bias against the course readings. AI offers breadth; students supply the evidence and the verdict. |
| ILO2 | Evaluate the applicability of learning theories to diverse educational contexts, learner needs and cultural settings. (Bloom: Evaluate) | Groups ask an AI to suggest how a theory would play out in a specific context, then interrogate the suggestion for cultural bias and pedagogical fit before accepting, modifying or rejecting it. |
| ILO3 | Collaborate in groups to construct a shared explanatory model of learning that synthesises multiple theoretical perspectives. (Bloom: Create) | AI-generated concept maps and explanations are used as raw material that groups critique, correct and re-draw; the concept-map builder records the group's own connections and marks any AI-sourced node. |
| ILO4 | Critically evaluate AI-generated explanations of learning theories for accuracy, completeness, bias and pedagogical limitations. (Bloom: Evaluate; AI literacy) | The AI Critique Lab, the chat-synthesis task and the AI interaction log turn evaluation into a concrete, logged habit: identify hallucinations, oversimplifications and biased framing, and cite the source that settles the question. |
| ILO5 | Exercise independent judgement by deciding when to adopt, modify or reject AI-generated content in constructing a theory-informed learning activity. (Bloom: Evaluate/Create; AI literacy) | Students document every decision - accepted, modified or rejected - with a rationale, and explain where human judgement (ethics, cultural sensitivity, pedagogical fit) took precedence over the AI's recommendation. |
AI is a starting point for interrogation, never a final authority. Every AI output in this lesson must be checked against course readings, evaluated for accuracy, completeness and bias, and then deliberately accepted, modified or rejected with a documented rationale. Human pedagogical, cultural and ethical judgement always takes precedence.
Section 2
Pre-Class Preparation
Before class you complete the flipped-learning preparation: read the assigned sources, watch the captioned video, take the 8-item adaptive pre-test and finish the AI engagement task. Work through <a href='https://openstax.org/books/psychology-2e/pages/6-introduction' target='_blank' rel='noopener'>OpenStax Psychology 2e, Chapter 6: Learning</a> and the <a href='https://www.simplypsychology.org/learning-theories.html' target='_blank' rel='noopener'>Simply Psychology overview of learning theories</a>, then bring your AI prompt, its output and your evaluation to class for the AI Critique Lab.
- Read <a href='https://openstax.org/books/psychology-2e/pages/6-introduction' target='_blank' rel='noopener'>OpenStax Psychology 2e, Chapter 6: Learning</a>, focusing on conditioning, observational learning and the shift toward cognitive accounts.
- Skim the <a href='https://www.simplypsychology.org/learning-theories.html' target='_blank' rel='noopener'>Simply Psychology overview of learning theories</a> to meet behaviourism, cognitivism, constructivism, social constructivism and humanism side by side.
- Watch the captioned instructor video Five Learning Theories in 12 Minutes (transcript and audio description provided) and note one idea per theory.
- Complete the 8-item adaptive diagnostic pre-test in your LMS; the feedback directs you to the exact reading section or video timestamp for anything you miss.
- Write a one-paragraph first answer to: how do behaviourism and constructivism differ in explaining how learning happens? What role does social interaction play, and how might cultural context shape which theory is most useful?
- Complete the AI engagement task: prompt an AI to summarise one theory, record the exact prompt and output, evaluate it for accuracy, completeness, missing nuance and bias, then post a short reflection (what it got right, what it oversimplified, which sources you would check, how training data might bias it, and where your own judgement was needed) to the LMS discussion board.
Tool Run the 8-item adaptive diagnostic pre-test in your LMS (Moodle / Canvas / Blackboard)
- In your LMS (Moodle, Canvas or Blackboard) open the quiz named Learning Theories - Diagnostic Pre-test (8 items).
- Confirm the quiz is set to adaptive, one-question-at-a-time mode with immediate feedback so each answer unlocks a hint.
- Attempt all eight items without looking up the answers; the quiz is diagnostic and not graded.
- For every wrong answer, read the adaptive feedback - it points you to a page in the OpenStax chapter or a timestamp in the video.
- Note which misconception category your errors fall into: constructivism means no guidance, behaviourism only applies to animals, or social constructivism ignores individual cognition.
- Revisit only the sections or timestamps you missed, then re-read the relevant reference card before class.
- Record your pre-test score - you will compare it with your post-test score in the synthesis phase.
- Complete the AI engagement task and post your prompt, the AI output and your evaluation to the LMS discussion board before the session.
- Flag any item whose feedback you disagree with; we debate those flagged items during the misconception review.
- Bring your AI prompt, its output and your written evaluation to class for the AI Critique Lab.
Section 3
Teaching & Learning Activities
Run sheet with timings and AI touchpoints: Introduction (15 min) - the Two Classrooms hook, a Mentimeter misconception review and a critique of an AI-generated lesson-plan excerpt. Development A (30 min) - the Theory Jigsaw. Development B (25 min) - AI-enabled critical interrogation. Development C (30 min) - collaborative AI-assisted problem-solving. Development D (20 min) - an interactive lecture on the key debates with think-pair-share, animated visualisations, drag-and-drop flashcards and a gamified poll. Synthesis and Closure (15 min) - post-test, peer-teaching summaries and the AI reflection discussion. The tool guides below give the step-by-step setup for each external platform used during the session.<details class='acc'><summary><span class='badge pill teal'>Tool</span> Mentimeter - misconception polling and the gamified Which Theory? poll</summary><div class='acc-body'><p><strong>Purpose:</strong> anonymously surface the most common pre-test misconceptions and run a fast gamified poll, so the whole 90-student cohort contributes without anyone being put on the spot. <strong>Learning objective:</strong> ILO 1 (analyse core theories) and ILO 2 (evaluate applicability) - students defend and revise their reasoning publicly.</p><ol class='steps'><li>Create a free account at <a href='https://www.mentimeter.com' target='_blank' rel='noopener'>create a Mentimeter poll</a> and start a new presentation titled Learning Theories - Misconception Review.</li><li>Add a Multiple Choice slide listing the three misconception statements from the pre-test (constructivism means no guidance; behaviourism only applies to animals; social constructivism ignores individual cognition).</li><li>Add a second slide type, Word Cloud, asking: In one word, what does learning mean in your classroom?</li><li>Add a final slide, the gamified Who/Which Theory? quiz, with four scenario questions drawn from the scenario quiz in the resources section.</li><li>In Settings, switch on Moderation off, and set the quiz slide to show a leaderboard between questions.</li><li>At the start of class, share the join code or QR code on screen; give students 60 seconds per slide.</li><li>Reveal the aggregated results, then ask pairs to challenge one another's answers before revealing the correct interpretation.</li><li>Export the results as an image to the LMS discussion so absent students can see the cohort's reasoning.</li></ol></div></details><details class='acc'><summary><span class='badge pill teal'>Tool</span> Padlet - collaborative brainstorming and peer critique wall</summary><div class='acc-body'><p><strong>Purpose:</strong> give each expert group a shared wall to post its theory summary, its AI critique and its open questions, so peers can comment asynchronously. <strong>Learning objective:</strong> ILO 3 (collaborate to construct a shared model) and ILO 4 (critically evaluate AI explanations) - the wall makes co-construction visible and auditable.</p><ol class='steps'><li>Sign in and build a wall at <a href='https://padlet.com' target='_blank' rel='noopener'>set up a Padlet board</a>, choosing the Shelf layout so each expert group gets its own column.</li><li>Name the columns Behaviourism, Cognitivism, Constructivism, Social Constructivism and Humanism.</li><li>In Settings, set Visitors can post, enable Comments and switch on Attribution so every post is signed.</li><li>Add a first post per column with the starter prompt: What does this theory claim, and where does the AI's summary go wrong?</li><li>During the jigsaw, each group posts a three-sentence summary plus one thing the AI got right and one thing it got wrong.</li><li>In the mixed-theory phase, students leave one comment per column evaluating another group's accuracy, clarity and evidence.</li><li>Use the wall's search as a live check: ask the class to find a post that would not survive source-checking.</li><li>Export the wall as PDF at the end of the session for the group portfolio.</li></ol></div></details><details class='acc'><summary><span class='badge pill teal'>Tool</span> Miro - collaborative concept mapping of theory integration</summary><div class='acc-body'><p><strong>Purpose:</strong> provide a shared digital whiteboard where groups build a concept map linking at least two theories to their activity design. <strong>Learning objective:</strong> ILO 3 (create a synthesised model) - the map externalises how theories connect to a concrete teaching problem.</p><ol class='steps'><li>Create a free team board at <a href='https://miro.com' target='_blank' rel='noopener'>open a Miro whiteboard</a> and name it Learning Theories - Group Concept Maps.</li><li>Drag five sticky-note colours onto the board and label them with the five theory names as a legend.</li><li>Add a central frame for the group's scenario (for example, a 15-minute climate-change activity) using the Frame tool.</li><li>Invite group members by link with edit rights, or share one device per group if devices are limited.</li><li>Ask each group to drag theory nodes around the scenario frame and connect them with labelled arrows explaining the link.</li><li>Require two nodes marked [AI] where the group used AI suggestions, plus a comment recording whether that suggestion was accepted, modified or rejected.</li><li>Use the Automation timer to keep the build to 20 minutes, then export each frame as PNG.</li><li>Ask groups to compare their map with an AI-generated concept map and annotate differences.</li></ol></div></details><details class='acc'><summary><span class='badge pill teal'>Tool</span> Google Docs - co-constructed theory summaries with comment-thread peer review</summary><div class='acc-body'><p><strong>Purpose:</strong> give every expert group a live, version-tracked document for its theory summary and AI critique. <strong>Learning objective:</strong> ILO 1 and ILO 3 - collaborative writing makes negotiation of meaning concrete, while Version History evidences individual contribution.</p><ol class='steps'><li>In <a href='https://docs.google.com' target='_blank' rel='noopener'>create a shared Google Doc</a>, set up one document per expert group from a template.</li><li>Structure the template with three headings: Summary in our own words, What the AI said, Our critique (accurate / oversimplified / biased).</li><li>Share with Group editors and set the class as Commenters.</li><li>Ask each member to add at least one sentence in their own words so contribution is visible in Version History.</li><li>In the critique section, require students to paste the exact AI prompt and output, then annotate with Suggesting mode.</li><li>Have a neighbouring group leave two comments: one on accuracy, one on clarity.</li><li>Check Tools, then Compare documents, to contrast the group's draft with the AI's original text.</li><li>Paste the final summary link into the Padlet column and submit the doc as portfolio evidence.</li></ol></div></details><details class='acc'><summary><span class='badge pill teal'>Tool</span> ChatGPT / Claude - structured critical interrogation of theory explanations</summary><div class='acc-body'><p><strong>Purpose:</strong> run the AI critique and comparison tasks with a repeatable prompt protocol that captures prompts, outputs and verdicts. <strong>Learning objective:</strong> ILO 4 and ILO 5 - students identify hallucinations and bias and decide when to override AI.</p><ol class='steps'><li>Open <a href='https://chat.openai.com' target='_blank' rel='noopener'>use ChatGPT for the critique task</a> or <a href='https://claude.ai' target='_blank' rel='noopener'>use Claude for the comparison task</a> in a browser tab.</li><li>Draft your own comparison on paper first - do not open the AI tool until your own view exists.</li><li>Paste a structured prompt such as: Compare behaviourism and social constructivism in a table, and state one limitation of each.</li><li>Copy the exact prompt and the exact output into your AI interaction log.</li><li>Annotate the output sentence by sentence, marking each claim accurate, oversimplified or wrong.</li><li>Verify at least one factual claim against the OpenStax chapter or Simply Psychology article.</li><li>Record what you accepted, modified or rejected from the output, and why human judgement was needed.</li><li>Repeat with a second tool and note whether the two models disagree - disagreement is a signal to slow down and check sources.</li></ol></div></details>
Students divide into five expert groups, one per theory (behaviourism, cognitivism, constructivism, social constructivism, humanism). Each expert group reads a short provided summary, uses an AI tool to generate an additional explanation, critically interrogates that output (what is accurate, what is oversimplified, what biases appear), and prepares a 3-minute peer-teaching summary on a shared Google Doc. Groups then reform into mixed-theory groups where each member teaches their theory. Roles rotate: facilitator, note-taker, AI critic and presenter. Timing: 12 minutes expert-group preparation, 15 minutes mixed-group teaching, 3 minutes debrief. This is a social-constructivist jigsaw - knowledge is distributed, negotiated and co-constructed.
Check each claim against your textbook and your own reasoning. A confident tone is not evidence of correctness.
Pick a prompt to see a simulated AI response, then write your critique.
- Require students to draft their own comparison on paper before opening any AI tool, so the AI supplements independent reasoning rather than replacing it.
- Make the boundary explicit: AI is appropriate for brainstorming, generating examples and drafting tables, but human judgement is essential for ethical decisions, cultural sensitivity and pedagogical fit.
- Avoid unreflective reliance by requiring an AI interaction log and critical annotation for every prompt a group uses.
- Model the four critical questions aloud when presenting the AI-generated lesson plan: which theory does this assume, what is missing, whose learning needs are excluded, and where does the AI make unsupported claims?
- When a group accepts an AI suggestion, ask what they verified and against which source before it earns a place in their design.
- Keep group roles rotating so no single student becomes the informal AI operator and everyone practises evaluation.
Section 4
Assessment Methods
Assessment is aligned to the outcomes and to the pedagogy: formative checks capture the quality of co-construction and AI-criticality during the session, while a group portfolio and a mirrored post-test provide summative evidence. AI use is permitted and required to be transparent: assessment tasks explicitly allow AI, but students must evaluate its accuracy and limitations, document their decision-making, explain what they modified or rejected, and articulate where human judgement took precedence.<div class='tablewrap' style='margin-top:12px'><table><thead><tr><th>Criterion</th><th>Exemplary</th><th>Proficient</th><th>Developing</th></tr></thead><tbody><tr><td>Theoretical accuracy</td><td>Accurately applies multiple theories with nuanced understanding</td><td>Applies theories correctly with minor gaps</td><td>Uses theories superficially or inaccurately</td></tr><tr><td>Collaborative knowledge building</td><td>Rich co-construction and peer negotiation evident</td><td>Some collaboration evident</td><td>Limited collaboration or individual work only</td></tr><tr><td>Critical AI engagement</td><td>Evaluates AI outputs rigorously for accuracy, bias and limitations</td><td>Evaluates AI outputs with some critical insight</td><td>Accepts AI outputs uncritically</td></tr><tr><td>Independent judgement</td><td>Clearly documents when and why AI was overridden</td><td>Some documentation of independent decisions</td><td>Little evidence of independent judgement</td></tr><tr><td>Communication</td><td>Clear, scholarly and well structured</td><td>Mostly clear with minor issues</td><td>Unclear or poorly organised</td></tr></tbody></table></div>
The post-test mirrors the pre-test item-for-item across the same three misconception categories - core concept, application, and AI and evidence - so you can compare your pre-test and post-test scores directly and see conceptual change, not just a number.
| Method | Weight | What it assesses |
|---|---|---|
| Pedagogy-aligned observation, live polling and exit ticket | Formative | Quality of group dynamics, peer dialogue and evidence of co-construction; a 3-2-1 exit ticket (3 insights, 2 questions, 1 AI critique). |
| Peer-assessment checklist | Formative | Each mixed-theory group assesses another group's theory summary for accuracy, clarity and collaboration. |
| AI-supported formative check | Formative | Students submit an AI-generated response alongside their own analysis; the instructor reviews the depth of critical engagement, not just the final answer. |
| Post-test | 10% | Individual, low-stakes check that mirrors the pre-test misconception categories to evidence conceptual change. |
| Project portfolio (group) | 60% | A theory-informed learning activity design, a rationale linking it to at least two theories, an AI interaction log, a reflection on accepted/modified/rejected AI content, and a statement on where human judgement took precedence. |
| AI feedback interrogation record | 30% | Documentation of how students interrogated, agreed with or overrode AI-generated feedback, with a written rationale and calibration-session notes. |
Section 5
Constructive Alignment Matrix
The matrix below shows how each intended learning outcome is served by a specific teaching activity, assessed by a specific task, grounded in the social-constructivist pedagogy, and supported by a particular critical AI integration.
| ILO | Learning activity | Assessment | Cognitive level |
|---|---|---|---|
| ILO1 - Analyse core learning theories | Theory jigsaw; interactive lecture | Peer-teaching summary; post-test | Analyse |
| ILO2 - Evaluate applicability to diverse contexts | Collaborative AI-assisted problem-solving | Project portfolio rationale | Evaluate |
| ILO3 - Collaborate to construct a shared model | Mixed-theory group work; concept mapping | Group portfolio; peer-assessment checklist | Create |
| ILO4 - Critically evaluate AI-generated explanations | AI-enabled critical interrogation | AI interaction log; critical annotation | Evaluate |
| ILO5 - Exercise independent judgement with AI | AI-assisted problem-solving; reflection discussion | Portfolio reflection on accepted/modified/rejected AI content | Evaluate / Create |
Section 6
Required Resources & Technology
The lesson runs on your LMS for the pre-test, discussion board and portfolio submission, on the pedagogy platforms Padlet, Google Docs, Mentimeter and Miro, and on AI tools such as ChatGPT or Claude with AI-powered feedback tools such as Gradescope or Turnitin Revision Assistant. The reference cards, drag-and-drop flashcard deck, gamified scenario quiz and concept-map builder below are the lesson's own interactive tools; the platform guides show how each external tool is set up. Physical and digital materials include printed case scenarios, concept-map templates, AI interaction log templates, assessment rubrics, and captioned videos with transcripts.<details class='acc'><summary><span class='badge pill teal'>Tool</span> Gradescope / Turnitin Revision Assistant - AI-supported formative feedback under instructor oversight</summary><div class='acc-body'><p><strong>Purpose:</strong> give draft portfolios initial formative feedback that is always reviewed by the instructor before release. <strong>Learning objective:</strong> ILO 5 - students interrogate AI feedback and document where they override it with a rationale.</p><ol class='steps'><li>Set up the assignment in <a href='https://www.gradescope.com' target='_blank' rel='noopener'>configure Gradescope for AI-assisted feedback</a> or in <a href='https://www.turnitin.com' target='_blank' rel='noopener'>use Turnitin Revision Assistant</a>.</li><li>Write an instructor-designed prompt or rubric that focuses the feedback on theoretical accuracy, collaborative knowledge building and critical AI engagement.</li><li>Upload the draft portfolios and let the tool generate initial comments against the rubric.</li><li>Review and edit every AI-generated comment yourself before releasing it - correct errors and soften or strengthen wording as needed.</li><li>Release the reviewed feedback and ask students three questions: do you agree with the AI's assessment; what might it have missed about your context or learners; where would you override it and why?</li><li>Require students to document any override with a written rationale in the AI decision log.</li><li>Hold a calibration session where the class compares AI feedback with human feedback on the same sample and rates which is more useful and why.</li></ol></div></details><details class='acc'><summary><span class='badge pill teal'>Tool</span> AI literacy and hallucination-check resources</summary><div class='acc-body'><p><strong>Purpose:</strong> equip students with concrete routines for detecting hallucination and bias rather than relying on intuition. <strong>Learning objective:</strong> ILO 4 - students identify hallucinations, omissions and biased framing in AI text.</p><ol class='steps'><li>Introduce the three-step check: does a source exist, does the source say this, and does the claim fit the wider evidence?</li><li>Model a live hallucination hunt by asking an AI for a citation about Vygotsky and searching for it.</li><li>Use an AI bias discussion prompt: whose perspectives and cultures are over-represented in this explanation?</li><li>Give students an AI interaction log template with columns for prompt, output, verdict and source checked.</li><li>Ask groups to swap logs and challenge one verdict each, citing evidence.</li></ol></div></details>
Reference cards
🃏 Flashcard deck — drag-and-drop matching
Drag an item chip (or tap it, then tap a slot) onto the matching description. Correct matches lock in green.
Item chips
Descriptions
🎮 Gamified scenario quiz
Identify which learning theory best explains each classroom scenario, then answer the peer-review prompt under the scenarios you attempted.
🧠 Concept-map builder Summative artefact
Choose a scenario, add at least three nodes, and record how each applies.
Add at least three nodes.
Section 7
Differentiation & Inclusivity
Multiple entry points, structured group roles and AI scaffolding ensure every learner can participate, while extension tasks stretch advanced learners and accessibility options remove barriers.
- Multiple entry points through visual, verbal and kinesthetic group roles so every learner has a route into the task.
- Peer mentoring within mixed-theory groups, with stronger students supporting explanation and critique.
- Structured roles for collaborative work: facilitator, note-taker, AI critic and presenter, rotated each activity.
- AI scaffolding for struggling learners through simplified explanations and step-by-step guidance, always checked against the readings.
- Accessibility accommodations: captioned videos with transcripts, screen-reader compatible documents, and options to contribute in written, oral or multimodal form.
- Flexible grouping and clear AI-use guidance to reduce anxiety for students with less AI experience, with optional tutorials available.
- Lead an AI bias analysis of a widely used educational AI tool and present the findings to the class.
- Design a complex interdisciplinary learning activity that integrates three or more theories with a documented AI critique.
- Mentor peers in critiquing AI-generated content, modelling the four critical questions.
- Compare how the same theory is framed across cultures and languages by prompting AI in more than one language and auditing the differences.
Section 8
Reflection & Improvement
Success indicators focus on the quality of collaboration and the depth of theory integration, and the session closes the loop with student and instructor reflection on whether AI supported critical thinking or quietly replaced it.
Portfolio
Critical Reflection Portfolio
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