Educational Psychology ยท Learning Theories
Introduction to Learning Theories
This 150-minute flipped lesson introduces first-year undergraduates to the five major learning theories โ behaviorism, cognitivism, constructivism, social constructivism and connectivism โ and to the AI literacy needed to judge machine-generated explanations of them. AI is positioned throughout as a cognitive partner to be interrogated, never an authority to be trusted: students learn to accept, modify or reject AI output with reasons.
Section 1
Intended Learning Outcomes
Each outcome is engineered so that AI integration is not an add-on but part of what success looks like โ including the judgement to question AI output.
| ILO | Description | ๐ค AI Integration |
|---|---|---|
| ILO1 | Analyse the core assumptions of behaviorism, cognitivism, constructivism, social constructivism and connectivism by comparing how each theory explains learning. | Students compare an AI summary of each theory against the reading and flag where the AI conflates, flattens or omits assumptions. |
| ILO2 | Evaluate the applicability of learning theories to authentic educational scenarios through collaborative dialogue and evidence-based reasoning. | AI is consulted for candidate solutions to a scenario; every AI suggestion must be justified with theoretical evidence or set aside. |
| ILO3 | Create a group concept map or mini-lesson design that applies one learning theory to a real-world learning problem, justifying design choices with theoretical principles. | AI acts as a design consultant; students document which AI ideas shaped the artefact and where human judgement overrode them. |
| ILO4 | Critically evaluate AI-generated explanations of learning theories for accuracy, completeness, bias and pedagogical limitations, and decide when to accept, modify or reject AI outputs. | Directly assessed in the AI Critique Lab: students cast accept / modify / reject verdicts on every AI claim and justify each verdict. |
| ILO5 | Document and reflect on independent reasoning and collaborative knowledge-building when using AI as a cognitive partner. | The AI decision log and the final reflection prompts capture where AI supported thinking and where it had to be overridden. |
AI-generated content is treated as an object of analysis, never an authority. For every AI claim students must state whether they accepted, modified or rejected it, and why human judgement did or did not take precedence.
Section 2
Pre-Class Preparation
Before the session students watch short explainers, complete a diagnostic pre-test that tags misconceptions, and interrogate one AI explanation of a learning theory of their choice.
- Read the Simply Psychology overview: <a href="https://www.simplypsychology.org/learning-theories.html" target="_blank" rel="noopener">Learning Theories</a>.
- Watch Khan Academy's explainer on <a href="https://www.khanacademy.org/test-prep/mcat/behavior/learning-and-memory/v/classical-conditioning" target="_blank" rel="noopener">Classical Conditioning</a>.
- Watch Crash Course Psychology: <a href="https://www.youtube.com/watch?v=8nz2dtv--ok" target="_blank" rel="noopener">How We Learn</a> (captions available).
- Complete the 8-item diagnostic pre-test before class โ it tags your misconceptions for in-class review.
- Complete the pre-class AI exploration task: prompt a chatbot to explain one learning theory and record what it explained well, what it oversimplified, omitted or biased, and one question you would ask a human expert to verify it.
- Bring one AI-generated claim to class for collective critique.
- Reflect on the guiding questions: How does social interaction shape what counts as learning? How does each theory position the learner, the teacher and knowledge? What evidence would convince you that one theory explains a situation better than another?
Tool Run the diagnostic pre-test in your LMS (Moodle / Canvas / Blackboard) and share results with Mentimeter
- Purpose: collect a diagnostic baseline and auto-tag misconceptions before class (supports ILO 1). In your LMS open the quiz tool โ Moodle: <em>Add an activity or resource > Quiz</em>; Canvas: <em>Quizzes > Classic Quizzes</em>; Blackboard: <em>Course Content > Assessments > Test</em> โ and create a test named <em>Learning Theories Pre-Test</em>.
- Enter the eight pre-test items from this lesson plan, and tag each one in the question bank with its misconception category ('core' or 'application') so a per-category report can be generated afterwards.
- For each item provide both feedback branches โ the 'why this answer is right' note and the 'why the picked option is wrong' note โ so every choice teaches.
- Enable adaptive/feedback settings: show question feedback immediately, disable re-attempts for the diagnostic run, and release results to students only after submission.
- Import the same question bank into <a href="https://www.mentimeter.com" target="_blank" rel="noopener">Mentimeter</a> or <a href="https://kahoot.com" target="_blank" rel="noopener">Kahoot</a> to run a gamified warm-up poll of the two or three hardest items live during the introduction (purpose: surface contested definitions of 'learning').
- Also create a <a href="https://padlet.com" target="_blank" rel="noopener">Padlet</a> wall, titled 'Learning is ___ according to ___ because ___', for the closure synthesis statements (purpose: make group co-construction visible).
- After class, export the aggregate per-tag results and bring them to the introduction for the misconception review; note for yourself which theory each misconception maps to.
Section 3
Teaching & Learning Activities
A three-phase run sheet (20 + 110 + 20 minutes) that moves from a provocative hook, through a chunked interactive lecture, a jigsaw, an AI Critique Lab and case-based concept mapping, to a synthesis that mirrors the pre-test misconceptions.
In 30 minutes, the 90 students form 15 expert groups of 6 (three groups per learning theory). Each group analyses one theory using the guiding questions and prepares a 3-minute peer-teaching summary. Groups then reshuffle so every new group contains one expert per theory, and each expert teaches their theory to peers. Knowledge is co-constructed through dialogue and distributed expertise โ the social-constructivist core of the lesson. Record your accept / modify / reject decisions about any AI help you use.
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.
- Use AI as a consultant, not an oracle: 'ask the AI, then check it.'
- Recognise when AI is appropriate versus when human judgement is essential.
- Avoid unreflective reliance: 'If you cannot explain it without AI, you do not yet own it.'
- Require every group to log each AI suggestion as accepted, modified or rejected, with a reason.
- Circulate during the AI tasks and probe: 'What did the AI leave out, and how do you know?'
Section 4
Assessment Methods
Formative assessment makes critical AI engagement visible and coachable; summative assessment pairs a co-constructed group artefact with an individual reflection on independent judgement.
The post-test re-uses the same misconception tags as the pre-test (for example 'learning is always observable behaviour' and 'constructivism means learning without guidance') so students can compare pre- and post-responses category by category.
| Method | Weight | What it assesses |
|---|---|---|
| Pedagogy-aligned observation | Formative | Collaboration quality, peer teaching and critical dialogue during the jigsaw and AI tasks. |
| Real-time polling & exit tickets | Formative | Mentimeter polls on theory identification; exit ticket: 'One thing AI got wrong today and how I know.' |
| Peer-assessment checklist | Formative | The accuracy, clarity and use of evidence in peers' theory explanations. |
| AI-supported formative check | Formative | Comparison of AI-generated responses with students' own analyses, reviewed for evidence of critical engagement. |
| Group concept map / mini-lesson design | Summative ยท 60% | Co-constructed application of a chosen theory to an authentic scenario (ILO 2 and ILO 3). |
| Individual critical reflection + AI decision log | Summative ยท 40% | Independent reasoning and documented accept / modify / reject decisions about AI output (ILO 4 and ILO 5). |
Section 5
Constructive Alignment Matrix
Every outcome is matched to a learning activity, an assessment method, a cognitive level and the specific way AI is integrated into it.
| ILO | Learning activity | Assessment | Cognitive level |
|---|---|---|---|
| ILO 1 โ Analyse core assumptions | Interactive lecture, jigsaw, flashcard deck | Pre/post-test, peer-assessment checklist | Analyse |
| ILO 2 โ Evaluate applicability to scenarios | Case-based application and group discussion | Group mini-lesson design (rubric) | Evaluate |
| ILO 3 โ Create concept map / mini-lesson | Concept mapping and design workshop | Summative group concept map / mini-lesson | Create |
| ILO 4 โ Critically evaluate AI outputs | AI Critique Lab and introduction critique | AI critique documentation and rubric criterion | Evaluate |
| ILO 5 โ Document independent reasoning with AI | Reflection discussion and exit ticket | Individual critical reflection and AI decision log | Reflect (metacognitive) |
Section 6
Required Resources & Technology
Your LMS, collaborative platforms and AI tools โ plus ready-made reference cards, a drag-and-drop flashcard deck and a gamified scenario quiz.
Reference cards
๐ Flashcard deck โ 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
๐ฎ Gamified scenario quiz
Identify which learning theory best explains each classroom scenario, then answer the peer-review prompt that appears under each item.
๐ง 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 through group roles, scaffolds for struggling learners, extensions for advanced learners, and AI-supported accessibility.
- Assign rotating group roles (facilitator, recorder, skeptic, AI critic) so every student has a clear entry point.
- Peer mentoring within jigsaw groups: stronger students model peer-teaching for others.
- AI tools provide simplified, step-by-step explanations for students who need extra scaffolding.
- Accessibility: AI-generated captions, text-to-speech and translation for the pre-class videos.
- Tiered guidance on AI use, written to accommodate students with very different levels of AI familiarity.
- Tactile and kinesthetic alternatives (printed scenario cards, sticky notes) for the concept-mapping task.
- Design a cross-theory integration model that combines two or more theories to explain one classroom problem.
- Lead a calibration session in which the class evaluates the quality of AI-generated feedback together.
- Use AI to explore a complex extension (for example connectivism in networked learning) and then critique how the AI framed it.
Section 8
Reflection & Improvement
Success indicators, student and instructor feedback loops, and concrete modification strategies for the next iteration.
Portfolio
Critical Reflection Portfolio
Everything you record here is stored locally on your device. Review it, then export it as evidence for your summative submission.
Nothing is uploaded anywhere โ all data stays in your browser's local storage.