Educational Psychology · Foundations of Education

Introduction to Learning Theories with AI Integration

A 150-minute, socially constructed exploration of Behaviorism, Cognitivism, Constructivism and Social Learning Theory — where students build shared understanding through dialogue, and learn to interrogate AI output with evidence, judgment and theoretical coherence.

⏱ 150 minutes 🎓 First-year undergraduates 👥 90 students 🗣 Social Constructivism 🔗 Constructive Alignment 🤖 AI in all sections 🌍 Internationally applicable
i

Lesson Overview & Design Rationale

Constructive alignment inside a social constructivist framework.

150
Minutes total
6
Intended Learning Outcomes
3
Core activities
4
Theories studied

Design context

This lesson implements Constructive Alignment within a Social Constructivist framework, where learners collaboratively construct knowledge through social interaction, dialogue, and shared meaning-making. Following Vygotsky's principles, the lesson emphasises the Zone of Proximal Development (ZPD) through peer collaboration and scaffolded learning.

AI integration is designed to enhance — not replace — collaborative knowledge-building, with explicit emphasis on developing students' critical AI literacy and independent judgment.

AI integration principle used throughout AI is positioned as a fallible co-inquirer, never an authority. Every AI interaction in this lesson follows the same three-move protocol: Prompt → Interrogate → Decide (Accept / Modify / Reject, with documented rationale).

Lesson timeline

20 min · 13% of class time
Introduction — "The Learning Theory Detective"
Scenario hook, collective knowledge wall, misconception analysis, real-world case, and the "AI Under the Microscope" evaluation sequence.
105 min · 70% of class time
Development — Jigsaw, Concept Mapping, Application Scenarios
Expert groups → cross-group teaching → human–AI collaborative concept map → authentic application with AI critique and Decision Logs.
25 min · 17% of class time
Synthesis & Closure — Post-test, Pitches, AI Reflection
Misconception-aligned post-test, 30-second elevator pitches, and structured meta-reflection on the AI–expertise relationship.
SegmentTimeShareDominant modeAI touchpoint
Introduction20 min13%Whole class + pairs + groupsAI Under the Microscope
Development105 min70%Expert groups → mixed groupsAI interrogation tables, concept-map critique, Decision Logs
Synthesis & Closure25 min17%Individual → groups → whole classAI reflection discussion
1

Intended Learning Outcomes (ILOs)

Upon completion of this lesson, students will be able to…

AnalyzeSocial Constructivist

ILO 1 — Analyze

Analyze the core principles of Behaviorism, Cognitivism, Constructivism, and Social Learning Theory through collaborative dialogue and peer explanation.

EvaluateSocial Constructivist

ILO 2 — Evaluate

Evaluate the applicability of each learning theory to authentic educational scenarios through group deliberation and consensus-building.

CreateSocial Constructivist

ILO 3 — Create

Create a collaborative concept map that synthesizes connections among learning theories, demonstrating shared understanding constructed through peer negotiation.

ApplySocial Constructivist

ILO 4 — Apply

Apply social constructivist principles to design a peer-teaching activity that demonstrates understanding of at least two learning theories.

AI LiteracyCritical Evaluation

ILO 5 — Critically evaluate AI

Critically evaluate AI-generated explanations of learning theories for accuracy, completeness, potential biases, and theoretical coherence, identifying instances where AI outputs require correction or supplementation.

AI LiteracyIndependent Judgment

ILO 6 — Demonstrate independent judgment

Demonstrate independent judgment in deciding when, whether, and how to incorporate AI-generated content into collaborative knowledge-building, articulating rationale for accepting, modifying, or rejecting AI suggestions.

🔗 Constructive alignment map — how each ILO is taught, practised and assessed
ILOTaught / practised inAssessed byFeedback source
ILO 1 AnalyzeJigsaw Phase 1 expert groups; Phase 2 cross-group teachingExpert materials; peer-assessment checklistPeer listeners + instructor circulation
ILO 2 EvaluateApplication Scenarios (Activity 3); misconception analysisDecision Log; group recommendation presentationInstructor debrief; peer critique
ILO 3 CreateCollaborative Concept Mapping (Activity 2)Concept map + gallery-walk critique protocolPeer critique cards; visual rubric
ILO 4 ApplyPeer-teaching roles; elevator pitchesPeer-assessment checklist; class vote on clearest explanationAudience response; instructor observation
ILO 5 Critically evaluate AIAll AI interaction tables; "AI Under the Microscope"AI Comparison submissions; Critical Engagement RubricRubric-coded annotations; instructor modelling
ILO 6 Independent judgmentDecision Logs; accept/modify/reject rationalesDecision Log quality; AI reflection discussionRubric feedback; peer questioning
Mark your own planning Tick each ILO once you have verified it is visible to students, rehearsed, and assessed.
2

Pre-Class Preparation

Flipped learning component — completed before the 150-minute session.

📚 Pre-reading materials

  • Schunk, D. H. (2020). Learning Theories: An Educational Perspective — Chapter 1: Introduction to Learning Theories.
  • Video: "Overview of Learning Theories" (Khan Academy).
  • Collaborative annotation task: students annotate the shared PDF using Hypothesis, tagging key concepts and posing questions for peers.
Why annotate socially? Annotation makes thinking visible before class. Questions posed by peers become the raw material for the in-class dialogue — the text becomes a shared object of negotiation rather than private reading.

✅ Pre-test / diagnostic quiz

10 questions with adaptive feedback, administered via the LMS.

Platform: Moodle or Canvas
Items: 10 adaptive
  • Questions assess prior knowledge of learning theories, misconceptions, and real-world applications.
  • Adaptive feedback directs students to specific review materials based on their responses.
  • Results are aggregated and anonymised for the in-class misconception analysis.
🧭 How to set this up in Moodle / Canvas
  1. Open your Moodle course or Canvas course and create a new quiz named "Learning Theories — Diagnostic".
  2. Enable adaptive/hint mode: in Moodle use Interactive with multiple tries; in Canvas use question-level feedback with a retry allowance.
  3. Author 10 items: 4 recall, 3 scenario-application, 3 misconception probes (see the three misconceptions in the Introduction).
  4. Attach feedback to every distractor that names a specific review resource (e.g. "Re-read Schunk pp. 1–14 before Thursday").
  5. Set the quiz to allow 2 attempts and highest score counted, so the diagnostic rewards persistence, not secrecy.
  6. Export the item statistics before class and screenshot the two most-missed items for the misconception analysis slide.

💬 Social constructivist guiding questions

Students respond to these in the annotation layer or on the LMS discussion board before class. They are revisited explicitly during the lesson.

1. How do you currently understand the process of learning?
2. What role does social interaction play in how people learn?
3. How might different learning theories explain the same classroom scenario differently?
AI-Enabled Pre-Class Engagement Task

AI Theory Exploration and Critique

Students engage with an AI tool (ChatGPT, Claude, or Gemini) using the structured protocol below.

Step 1 — AI Interaction

Prompt to paste "Explain the difference between Behaviorism and Constructivism in learning theory. Provide examples of how each would approach teaching multiplication to a 3rd grader."

Step 2 — Critical Observation

Record observations using this template (click any cell to type; your entries save automatically in this browser).

AspectAI Response SummaryYour Evaluation
Accuracy of theoretical descriptions
Completeness (what's missing?)
Potential biases or oversimplifications
Quality of practical examples

Step 3 — Guided Reflection Prompts

  1. Did the AI accurately represent both theories? What evidence supports your judgment?
  2. What nuances or complexities did the AI omit?
  3. Did the AI show any bias toward one theory over another? How could you tell?
  4. What questions would you ask the AI to probe deeper?
  5. Where did you need to rely on your own knowledge rather than accepting AI output?

Step 4 — Bring to Class

Submit your completed template and one AI-generated response you found problematic, along with your critique. These submissions become the first evidence for the Critical Engagement Rubric.

3

Teaching & Learning Activities

150 minutes across three phases: Introduction (20), Development (105), Synthesis & Closure (25).

Introduction20 minutes · 13% of class time

Part A — Social Constructivist Hook: "The Learning Theory Detective"

Students form groups of 4–5. Each group receives a classroom scenario (e.g. "A teacher uses flashcards and rewards to teach vocabulary").

Discussion prompt "What assumptions about learning does this teaching approach make?"

Groups share initial hypotheses via Padlet, building a collective knowledge wall. Full setup steps are in the External Tools section.

Part B — Pre-Test Review with Misconception Analysis

The instructor displays aggregated pre-test results (anonymised), highlighting common misconceptions:

Misconception 1
"Constructivism means students learn without any guidance."
Misconception 2
"Behaviorism is outdated and never useful."
Misconception 3
"Learning theories are mutually exclusive."

Social constructivist approach: groups discuss why these misconceptions exist and how peer dialogue can address them. Each group posts one strategy for addressing a misconception.

🧠 Instructor notes: why these three misconceptions persist
  • Misconception 1 conflates constructivism with discovery learning. Vygotsky's ZPD and Bruner's scaffolding show guidance is central — the question is what kind of guidance, and when it fades.
  • Misconception 2 treats theories as fashions rather than tools. Behaviourist principles (reinforcement, feedback timing, fluency-building) remain structurally present in adaptive learning software, spaced-repetition apps, and classroom routines.
  • Misconception 3 mistakes a taxonomy for a set of rival teams. Theories differ in unit of analysis (observable behaviour, mental schema, socially negotiated meaning, modelled observation) rather than in what they claim is true.

Part C — Real-World Connection

Opening case "A university professor notices students struggle with statistics. How would each learning theory diagnose and address this problem?"

This frames the lesson's practical relevance: theory is a diagnostic lens, not a memorised list.

Part D — AI-Generated Scenario Introduction

Activity: "AI Under the Microscope"

The instructor displays an AI-generated response to the prompt:

Displayed AI prompt"Explain how Vygotsky's Zone of Proximal Development applies to collaborative learning."

Process

1. Individual Evaluation (2 min) — Students silently read and note strengths/weaknesses.
2. Pair Discussion (3 min) — Partners compare evaluations, identifying agreements and disagreements.
3. Group Synthesis (3 min) — Groups compile top 3 strengths and top 3 limitations.
4. Class Sharing (5 min) — Groups share findings; instructor models critical questioning:
  • "The AI mentions scaffolding — but does it explain how scaffolding differs from direct instruction?"
  • "Is there any citation or evidence for these claims?"
  • "What would Vygotsky himself critique about this explanation?"
Instructor modelling script "I notice the AI says 'students learn best collaboratively' — but that's an oversimplification. Vygotsky emphasised that collaboration must be within the ZPD, not just any group work. The AI missed this crucial nuance."
📝 Group capture sheet — top 3 strengths & top 3 limitations (saves locally)

Top 3 strengths of the AI response

Top 3 limitations of the AI response

Development105 minutes · 70% of class time

Part E — Development Activities

Three sequenced activities, each with social constructivist structure and an embedded AI-critique protocol.

Activity 1 · Jigsaw Expert Groups — 35 minutes Social constructivist structure: students become "experts" on one theory, then teach peers.

Phase 1 — Expert Group Formation (15 minutes)

  • Class divides into 4 "expert groups": Behaviorism, Cognitivism, Constructivism, Social Learning Theory.
  • Each group receives a theory summary and guiding questions.
  • Groups collaboratively construct a shared understanding, preparing to teach others.
AI-Enabled Learning Activity

Expert Group AI Interaction

Step 1 — AI Prompting

Groups prompt AI with:

"Explain [assigned theory] including: key theorists, core principles, classroom applications, and common criticisms."
Step 2 — Critical Interrogation

Groups systematically evaluate AI output:

Evaluation CriterionAI ResponseGroup AssessmentAction Taken
Theoretical accuracy Accurate / Partially Accurate / Inaccurate Accept / Modify / Reject
Completeness of key concepts Complete / Missing elements Add:
Representation of criticisms Balanced / Biased / Superficial Supplement:
Quality of examples Strong / Weak / Problematic Replace with:
Step 3 — Documentation
  • What AI got right (with evidence)
  • What AI got wrong or oversimplified (with corrections)
  • What AI omitted entirely (with additions)
  • Rationale for decisions to accept, modify, or reject AI content
Step 4 — Enhanced Teaching Materials

Groups create their teaching materials by improving upon AI output, not simply copying it.

Phase 2 — Cross-Group Teaching (20 minutes)

  • New groups form with one "expert" from each theory.
  • Each expert teaches their theory for 4 minutes.
  • Listeners ask clarifying questions and take notes.
  • Experts explicitly share: "Here's what AI told us, and here's how we corrected or improved it."

Instructor Guidance on AI Criticality (embedded throughout)

Circulate and prompt: "Did you verify that claim? How do you know the AI is correct?"
Model scepticism: "AI can sound confident while being wrong. What's your evidence?"
Emphasise: "Your expertise comes from your analysis, not from AI's output."
Activity 2 · Collaborative Concept Mapping — 30 minutes Social constructivist structure: groups negotiate shared meaning through concept map creation.

Process

  1. Groups of 4–5 receive large paper or use a digital tool (Miro, Lucidchart).
  2. Groups create a concept map showing:
    • Core principles of each theory
    • Connections and contrasts between theories
    • Real-world applications
    • Key theorists and their contributions
AI-Enhanced Concept Mapping

Step 1 — AI Suggestion Generation

"Create a concept map outline connecting Behaviorism, Cognitivism, Constructivism, and Social Learning Theory. Include key relationships."

Step 2 — Critical Evaluation of AI Structure

  • Groups evaluate AI's proposed connections: Are they accurate? Complete? Meaningful?
  • Identify missing connections or incorrect relationships.
  • Discuss: "What does AI misunderstand about how these theories relate?"

Step 3 — Human–AI Collaborative Map Creation

  • Groups create their own concept map, using AI suggestions as a starting point but critically revising.
  • Document: "We kept X from AI because… We changed Y because… We added Z because AI missed…"

Step 4 — Gallery Walk and Peer Critique (10 minutes)

  • Groups display maps; peers provide feedback using a structured critique protocol.
  • Critique prompts include: "Where did this group demonstrate critical engagement with AI?"
🗺 Reference — a defensible "expert" map of the four theories
TheoryUnit of analysisKey theoristsRelationship to others
BehaviorismObservable behaviour; stimulus–response–reinforcementPavlov, Watson, Thorndike, SkinnerDenies or brackets internal cognition; strong overlap with Cognitivism on feedback timing
CognitivismMental schemas, encoding, working memory loadPiaget (mechanism), Ausubel, Gagné, SwellerRehabilitates the "black box"; scaffolds Constructivism's account of prior knowledge
ConstructivismMeaning constructed from prior knowledge and experiencePiaget, Bruner, Vygotsky (social variant), von GlasersfeldRadical vs. social branches split over whether meaning is individual or negotiated
Social Learning TheoryObservation, modelling, vicarious reinforcement, self-efficacyBanduraBridges behaviourism and cognitivism; underpins Social Cognitive Theory
Teach the split, not just the list AI frequently flattens Vygotsky into generic "group work". Vygotsky's claim is stronger: higher mental functions originate in social interaction and are internalised. That is why the map's most valuable arrows are the contested ones — they are where student judgment shows.
Activity 3 · Application Scenarios with AI Critique — 25 minutes Social constructivist structure: groups apply theories to authentic problems through collaborative reasoning.

Scenario-Based Learning

Each group receives a complex educational scenario, e.g. "A middle school struggles with student engagement in online learning."

AI-Integrated Application Process

Step 1 — AI Solution Generation

"Using [assigned theory], propose three strategies to address [scenario]."

Step 2 — Critical Analysis Protocol

Groups evaluate each AI-proposed strategy:

AI StrategyTheoretical AlignmentPractical FeasibilityPotential IssuesGroup Decision
Strategy 1 Strong/Weak alignment because… High/Medium/Low because… Adopt / Modify / Reject
Strategy 2 Strong/Weak alignment because… High/Medium/Low because… Adopt / Modify / Reject
Strategy 3 Strong/Weak alignment because… High/Medium/Low because… Adopt / Modify / Reject

Step 3 — Human Judgment Documentation

Groups create a "Decision Log" documenting:

  • Which AI suggestions were adopted and why
  • Which were modified and how
  • Which were rejected and why
  • What human expertise added that AI could not provide

Step 4 — Group Presentation (brief, 2 min per group)

Groups present their final recommendations, explicitly addressing: "Where did human judgment override or improve upon AI suggestions?"

Cross-Pedagogy Interactive Elements

Think-Pair-Share (embedded in transitions)

  • "Which theory best explains your own learning in this class so far?"
  • "How has AI helped or hindered your understanding today?"

Digital Flashcards

Flashcard Set: Learning Theory Key Concepts (Social Constructivism) Interactive cards with theory terms, definitions, and application examples. Students quiz each other in pairs, explaining concepts in their own words. Tap a card below to flip it.

Gamified Poll

Quiz: Learning Theory Scenarios — Social Constructivism Scenario-based multiple choice via Mentimeter. Students discuss answers in groups before responding; immediate results spark peer discussion about reasoning.

Animated Visualization

  • Short animation showing how each theory conceptualises the learning process.
  • Followed by group discussion: "What does this visualization emphasise? What does it leave out?"

Scaffolding and Social Constructivist Integration

Chunked content with worked examplesEach theory is introduced with (1) core principles, (2) a worked classroom example, (3) a group discussion prompt.
Concept maps for theory integrationProgressive concept-map building throughout the lesson; groups add to a class-wide map after each activity.
Peer scaffoldingMore knowledgeable peers support others within the ZPD; structured peer-teaching roles rotate so every student experiences both teaching and learning.
Gradual release of responsibility Instructor models analysis → Groups practise with guidance → Groups apply independently.
Synthesis & Closure25 minutes · 17% of class time

Part F — Synthesis and Closure

1. Post-Test Aligned with Initial Misconceptions (10 minutes)

  • Revisit the three misconceptions from the introduction.
  • Individual quiz (5 questions) addressing each misconception.
  • Immediate peer discussion: "How has your understanding changed?"
🧪 In-class post-test — 5 questions with instant feedback (attempt it here)

Answer individually, then compare reasoning with your group. Your choices are saved locally.

2. Peer-Teaching Summaries (5 minutes)

  • Each group creates a 30-second "elevator pitch" summarising their theory.
  • Groups share via rapid-fire presentations.
  • Class votes on the clearest explanation (gamified via Mentimeter).
🎤 Elevator-pitch structure (30 seconds) — scaffold for students
  1. Hook (5s): one sentence naming the theory's central claim.
  2. Evidence (10s): one classic study or theorist + what it showed.
  3. Classroom use (10s): one concrete teaching move this theory justifies.
  4. Limitation (5s): the strongest criticism — and your response to it.
AI twistEach pitch must name one thing AI told the group that they corrected.

3. AI Reflection Discussion (10 minutes)

Structured class discussion:

Prompt 1"How did AI support or hinder your learning today?" — Groups discuss for 2 minutes, then share key points.
Prompt 2"When did you need to override AI suggestions? What informed your decision?" — Students share specific examples from their Decision Logs.
Prompt 3"What did you learn about the relationship between AI and human expertise?" — instructor facilitates synthesis.
Instructor synthesis "AI can provide information, but you provide judgment, context, and critical evaluation. Your expertise is irreplaceable."

Student sharing: volunteers share instances where AI was helpful vs. misleading; the instructor highlights exemplary critical engagement.

4. Preview of Next Session

  • Next topic: "Applying Learning Theories to Instructional Design".
  • Pre-class task: students will use AI to draft an instructional plan, then critique it using today's frameworks.
4

Assessment Methods

Formative (during class) and summative (aligned to the six ILOs), with AI-supported evidence collection.

Formative Assessment — During Class

Social Constructivist Observations

  • Instructor circulates, observing group dynamics, quality of collaborative dialogue, and evidence of shared meaning-making.
  • Observation checklist includes: "Do groups negotiate meaning or simply divide tasks?" and "Do students build on each other's ideas?"

Real-Time Polling and Exit Tickets

  • Mentimeter polls after each activity to gauge understanding.
  • Exit ticket: "One thing I learned from a peer today that I couldn't have learned alone."

Peer-Assessment Checklists

During cross-group teaching, listeners complete peer-assessment forms evaluating:

CriterionYes / Partly / NoEvidence note
Clarity of explanation☐ Yes   ☐ Partly   ☐ No
Accuracy of theoretical content☐ Yes   ☐ Partly   ☐ No
Evidence of critical engagement with AI outputs☐ Yes   ☐ Partly   ☐ No

AI-Supported Formative Checks

AI Comparison Submissions Students submit AI-generated responses alongside their own analyses. The instructor reviews for evidence of critical engagement:
  • Did students identify AI inaccuracies?
  • Did they document modifications with rationale?
  • Did they demonstrate independent judgment?
Formative Rubric

Critical Engagement Rubric

CriterionEmergingDevelopingProficientExemplary
AI Output Evaluation Accepts AI uncritically. Identifies obvious errors. Systematically evaluates accuracy and completeness. Evaluates accuracy, bias, and theoretical coherence with evidence.
Documentation of Decisions No documentation. Lists decisions without rationale. Documents decisions with basic rationale. Documents decisions with detailed rationale and evidence.
Independent Judgment Relies on AI for answers. Occasionally questions AI. Consistently evaluates AI against own knowledge. Articulates clear criteria for when to accept, modify or reject AI, and applies them consistently across tasks.
Theoretical Coherence of Revisions Revisions are cosmetic or absent. Revisions correct surface facts. Revisions align with the theory's core claims. Revisions resolve genuine tensions between theories and justify the resolution with literature.
Collaborative Knowledge-Building Divides tasks; no negotiation. Shares findings without building on them. Builds on peers' ideas to reach shared understanding. Actively negotiates meaning, reconciles disagreement, and synthesises peers' reasoning into a defensible shared position.
How to use it formatively Highlight the descriptor the evidence actually meets (not the one you hope it meets), then write one forward-facing sentence: "To move to Proficient, add a citation that supports your correction of the AI's claim about Bandura."
Summative Assessment — After Class

Aligned to all six ILOs

TaskWeightILOs assessedWhat it evidences
Annotated AI Comparison Portfolio30%ILO 5, ILO 6Selected AI outputs plus the student's corrections, bias notes, and accept/modify/reject rationales.
Collaborative Concept Map + Individual Rationale25%ILO 1, ILO 3Group artefact plus an individual 400-word account of the negotiation process and one disagreement resolved.
Applied Scenario Report with Decision Log30%ILO 2, ILO 6Theory-grounded recommendations, with explicit tracking of where human judgment overrode AI.
Peer-Teaching Reflection15%ILO 4Design of the peer-teaching sequence, plus reflection on how it enacted ZPD principles.
Academic integrity framingUsing AI is permitted and expected; undeclared, uncritical use is not. The portfolio format makes critical engagement the assessed skill, which converts a misconduct risk into a learning outcome.
Inclusive assessmentEvery task has a text, diagram, or spoken-evidence route. Group artefacts are always paired with an individual rationale to keep marks attributable.
5

Feedback Design

Feedback as dialogue — timed, specific, and increasingly self-generated.

Feedback loop 1

Immediate · In-class

  • Mentimeter results shown instantly to spark peer discussion about reasoning, not just correctness.
  • Instructor circulates and gives in-the-moment prompts ("Did you verify that claim?").
  • Peer-assessment forms returned to teaching groups within the same session.
Feedback loop 2

Post-class · Instructor

  • AI Comparison submissions returned with two rubric descriptors highlighted and one forward action.
  • Pattern-level feedback (whole-class, anonymised) published to the LMS within 48 hours.
  • Exemplary critical engagement is shared (with permission) as a worked model.
Feedback loop 3

AI-mediated · Peer & self

  • Students use an AI tool as a critique partner: prompting it to argue against their own conclusion, then evaluating whether its objections hold.
  • Peer feedback uses the same rubric language, so students internalise the criteria.
  • Self-assessment: "Which descriptor am I on, and what is my evidence?"

Feedback quality checklist (Hattie & Timperley: Feed Up, Feed Back, Feed Forward)

DimensionQuestion to answer in feedbackExample sentence stem
Feed Up (Where am I going?)Is the goal restated in student-accessible terms?"The goal here is to show that you can judge AI output, not just reproduce it…"
Feed Back (How am I going?)Is there specific evidence from the student's own work?"In your Decision Log you rejected Strategy 2 — your reason cites…"
Feed Forward (Where to next?)Is there one concrete, doable next action?"Next time, add the source that supports your correction, as in Schunk p. 12."
AI-assisted feedback protocol If AI is used to help draft feedback, the instructor must: (1) verify every claim against the student's submitted work; (2) remove generic praise; (3) ensure feedback names evidence the student will recognise; (4) never send AI-drafted feedback unreviewed. This mirrors exactly the critical stance students are being taught — the instructor models it.

Exit-ticket feedback capture

Two prompts, collected before students leave (paper or Google Forms):

Prompt 1"One thing I learned from a peer today that I couldn't have learned alone."
Prompt 2"One thing I'll do differently because of an AI limitation I found today."
6

External Tool Integration

Step-by-step setup for every external tool named in this lesson, tailored to learning theories.

How these cards work Each card gives the tool's purpose in this lesson, the learning objectives it serves, and numbered setup steps. Expand a card to follow the steps while you build the session.
🧱 Padlet — Collective knowledge wall (Introduction hook)
Purpose: Capture groups' initial hypotheses about what a teaching approach assumes about learning, and build a shared visual wall that the whole class can see and reference all lesson.
Learning objectives served: ILO 1 (analyze), ILO 2 (evaluate), and the social constructivist goal of making collective thinking public.
  1. Go to padlet.com and sign in (or create a free account). Use an education account if your institution provides one.
  2. Click "Make a Padlet" and choose the Wall or Columns format. Columns works well: one column per classroom scenario.
  3. Title it "Learning Theory Detective — What does this approach assume about learning?"
  4. In Settings, enable Posting → Comments so peers can question each other's hypotheses, and enable Reactions so the class can upvote the most defensible hypotheses.
  5. Set Privacy → Secret URL and set Moderation to off (you want speed, not gatekeeping, in the first 8 minutes).
  6. Copy the QR code and the join link and display them on the projector as groups of 4–5 form.
  7. As groups post, prompt: "Name the theory your hypothesis is closest to — and the assumption it makes."
  8. After posting, sort the wall by reactions and use the top three posts to bridge into the misconception analysis.
📊 Mentimeter — Live polls & gamified voting
Purpose: Surface reasoning in real time after each activity, run the gamified theory poll, and host the class vote on the clearest elevator pitch.
Learning objectives served: ILO 2 (evaluate through deliberation), ILO 4 (apply through peer-teaching), plus formative assessment of misconceptions.
  1. Go to mentimeter.com and sign in. Create a new presentation titled "Learning Theories — Live Check".
  2. Add a Multiple Choice slide: "Which theory best explains the flashcards-and-rewards scenario?" with the four theory names as options.
  3. Add a Word Cloud slide: "One word for how AI affected your learning today."
  4. Add a Quiz Competition slide set (use the scenario items from the post-test) so results display as a leaderboard — this is the gamified poll.
  5. Add a Ranking slide for the elevator-pitch vote: groups rank the four pitches on clarity.
  6. In settings, enable "Show results to audience" after each vote — the discussion about why peers chose differently is the point.
  7. Before class, test the voting on your own phone using the join code at menti.com.
  8. After the poll, freeze the display and ask each group to justify why a peer's reasoning was persuasive — this converts polling into dialogue.
Free-tier noteThe free plan limits the number of slide types and question count per presentation. Split slides across two presentations if needed; create both before class.
🎮 Kahoot! — Fast-paced theory consolidation
Purpose: Rapid retrieval practice on theory terminology and theorists, and a high-energy consolidation game at the end of the Jigsaw.
Learning objectives served: ILO 1 (analyze core principles), reinforcement of key theorists for the concept map.
  1. Go to kahoot.com and sign in as a teacher. Open Kahoot! Studio.
  2. Create a new quiz titled "Learning Theories: Who Said It?" with 8–10 questions.
  3. Write questions that force theorist-concept matching: "Who is associated with the Zone of Proximal Development?" (Vygotsky).
  4. Add two "misconception trap" questions: e.g. "True or false: Constructivism means teachers should never give guidance." (False.)
  5. Set the timer to 20 seconds per question — fast enough to be a game, slow enough to think.
  6. In settings, turn on Answer Streak and turn off randomised answer order only if your questions depend on option order.
  7. Project the game on the main screen; students join at kahoot.it or the Kahoot app with the PIN.
  8. After each question, pause and ask a group to explain the reasoning behind the distractors — retrieval practice plus metacognition.
Class size 90Kahoot works at scale, but pace the discussion deliberately: pause after every second question to avoid it becoming pure entertainment.
📝 Google Forms — Pre-test, exit tickets & peer assessment
Purpose: Collect the diagnostic pre-test, the exit ticket, and structured peer-assessment data with automatic aggregation.
Learning objectives served: Formative assessment across ILOs 1–6; aggregation of misconception data for the introduction.
  1. Open Google Forms and click Blank quiz.
  2. Title it "Learning Theories — Pre-Class Diagnostic" and add 10 questions using Multiple choice and Short answer.
  3. For each question, use the Answer key panel to set the correct answer and add feedback for correct/incorrect that names a specific review resource.
  4. In Settings, enable Make this a quiz, set Release grade → Immediately after each submission, and enable Respondent can see missed questions.
  5. Also create a second short form for the exit ticket with two Paragraph questions (peer learning + AI limitation).
  6. Create a third form for peer assessment with three Linear scale (1–3) items matching the peer-assessment checklist criteria.
  7. Link all three forms in your LMS module page so students reach them from one place.
  8. Before class, open the Responses → Summary tab of the diagnostic and screenshot the two most-missed items for the misconception analysis.
🖍 Jamboard / whiteboard — Collaborative concept mapping
Purpose: Give groups a shared surface to negotiate a concept map in real time — sticky notes, arrows, and images — then run the gallery walk across boards.
Learning objectives served: ILO 3 (create a collaborative concept map), ILO 4 (peer teaching).
  1. Open Jamboard (or your institution's whiteboard tool) and create one board per group — for 90 students, 18–22 boards.
  2. Name each board "Group [n] — Theory Map" and add a frame pre-seeded with the four theory names in the corners.
  3. Pre-place four sticky notes: Core principles · Theorists · Applications · Criticisms as a scaffolding frame for groups that need it.
  4. Share each board using Share → Anyone with the link can edit, and distribute the board links through the LMS group pages.
  5. Instruct groups: place AI's suggested relationships in one colour and the group's own corrections in a second colour.
  6. For the gallery walk: display all boards in a grid, then give each group three peer-critique sticky notes using the protocol prompt "Where did this group demonstrate critical engagement with AI?"
  7. Export the final boards as PDF (⋮ → Download frame as PDF) to attach to the class-wide map.
If Jamboard is unavailableUse Miro or Lucidchart with an identical workflow — pre-seeded templates, shared edit links, and a colour rule for AI-sourced vs. human-added content.
🖊 Hypothesis — Social annotation of the pre-reading
Purpose: Make pre-class thinking visible by having students annotate the Schunk chapter collaboratively, tagging key concepts and posing questions that seed in-class dialogue.
Learning objectives served: ILO 1 (analyze), and preparation for every in-class activity.
  1. Go to web.hypothes.is and create an account (or enable the Hypothesis app inside your LMS).
  2. Create a private group for the class and copy its group ID.
  3. Host the Schunk chapter PDF on an accessible URL (or embed it in the LMS with the Hypothesis LTI).
  4. Give students a tagging convention: #principle, #theorist, #example, #question.
  5. Set the task: at least two tagged concepts and one question directed at a peer.
  6. Before class, read the #question tags and select three to open the session with — these become the live, authentic discussion anchors.
  7. In class, display the annotation sidebar and ask the students who wrote the questions to explain what prompted them.
🤖 AI Assistants (ChatGPT / Claude / Gemini) — The critical-inquiry partner
Purpose: The central AI literacy instrument of the lesson. Every AI interaction in this plan routes through one of three prompts, followed by the Prompt → Interrogate → Decide protocol.
Learning objectives served: ILO 5 (critically evaluate AI), ILO 6 (independent judgment).
  1. Direct students to a chat assistant — ChatGPT, Claude, or Gemini — on their own device or a lab machine.
  2. Give them the three lesson prompts verbatim: the pre-class comparison prompt, the expert-group prompt ("Explain [assigned theory] including…"), and the scenario prompt ("Using [assigned theory], propose three strategies…").
  3. Instruct students to save the AI output in a document and paste it into the left-hand column of the relevant table before writing a single word of evaluation — this prevents post-hoc rationalising.
  4. Apply the interrogation criteria: accuracy, completeness, representation of criticisms, quality of examples.
  5. Require an explicit Accept / Modify / Reject decision for each element, with a one-sentence rationale.
  6. Run the second-order test: ask the AI "What are you least confident about in that answer?" and evaluate whether its self-assessment is itself reliable.
  7. Collect the portfolio: AI output + annotations + decisions + rationale, submitted as the summative Annotated AI Comparison Portfolio.
Two failure modes to pre-empt (1) Trust collapse — students assume AI is right because it sounds fluent. (2) Trust annihilation — students dismiss all AI output. Both are inaccurate. The target is calibrated judgment, evidenced by a documented decision trail.
🕸 Miro / Lucidchart — Digital concept-mapping alternative
Purpose: Digital concept mapping with traceable revision history, useful for large classes and for capturing the AI-vs-human edit trail.
Learning objectives served: ILO 3 (create), ILO 6 (documenting decisions).
  1. Sign in at Miro or Lucidchart and create a team board.
  2. Duplicate a pre-built concept-map template for each group so every group starts from the same scaffold.
  3. Apply the colour convention: purple = AI-suggested, teal = group-authored, red = rejected with note.
  4. Enable commenting so peers can attach critique during the gallery walk directly onto the map.
  5. Use the board's version history to show students the revision trail — evidence of negotiation, not just the final artefact.
  6. Export each final map as PDF/PNG and paste into the class-wide progressive map.
🎓 Moodle / Canvas LMS — Delivery, diagnostics & submissions
Purpose: Single point of access for the pre-test, annotations, AI Comparison submissions, rubrics, and item-level feedback.
Learning objectives served: all ILOs — the assessment backbone.
  1. Create a lesson module in Moodle or Canvas named "Learning Theories — Session 1".
  2. Add the diagnostic quiz with adaptive feedback, then the Hypothesis-annotated reading, then the AI Theory Exploration submission dropbox.
  3. Upload the Critical Engagement Rubric as a rubric attached to the submission dropbox so students see criteria before submitting.
  4. Use SpeedGrader (Canvas) or the grading form (Moodle) to highlight rubric descriptors and attach audio or text comments.
  5. Build a "fake student" preview submission in each dropbox to test the rubric and feedback flow before the session.
  6. After grading, publish anonymised pattern feedback as an announcement within 48 hours.
7

Resources, References & Session Checklist

Materials, literature, and a final pre-flight check.

📖 Core references

  • Schunk, D. H. (2020). Learning Theories: An Educational Perspective (Chapter 1: Introduction to Learning Theories).
  • Bandura, A. — Social Learning Theory / Social Cognitive Theory.
  • Piaget, J. — genetic epistemology and constructivist mechanism.
  • Vygotsky, L. S. — Mind in Society: ZPD, scaffolding, internalisation.
  • Skinner, B. F. — operant conditioning and reinforcement.
  • Hattie, J. & Timperley, H. — the power of feedback (Feed Up / Feed Back / Feed Forward).
  • Biggs, J. — constructive alignment.

🎬 Media

✅ Final pre-flight checklist