| Tool | Description | Pedagogy Link | AI Integration |
|---|---|---|---|
| Pre-Class Quiz Mentimeter |
5-question diagnostic submitted before class; results reviewed at start of session to surface misconceptions | Activates prior schema; provides baseline for learning comparison | — |
| AI Journal Check AI-Focused |
Instructor reviews students' pre-class AI reflection journals for evidence of critical evaluation of AI limitations | Self-assessment of initial AI skepticism; serves as discussion seed in 3a | Students demonstrate whether they identified AI inaccuracies or biases |
| Group AI Example Critiques Segment 1 |
Worksheet evaluating AI's classical conditioning examples — label US/UR/NS/CS/CR, flag operant mislabels, identify cultural limits | Peer-driven error identification and co-construction of accurate labels | Students document mistakes in AI outputs; justify accept/reject decisions |
| Operant Design Plan Segment 2 |
Groups' revised behaviour modification plans posted to Padlet with peer comments via gallery walk | Peer feedback via gallery walk; distributed expertise | Evidence of human "override" of AI-generated plans; ethical reasoning documented |
| Synthesis Poster + 1-min Presentation Segment 4 |
Group poster with analogy, 2 real-world applications, 1 limitation, and AI slogan adopt/modify/reject decision | Peer-assessment using structured rubric; ZPD group synthesis | Groups explicitly explain their AI adopt/modify/reject decision to the class |
| Exit Ticket 3 Questions |
(1) One-word takeaway; (2) Continuing question; (3) Did AI help you learn today? Why/why not? | Reflective self-assessment; informs next session adjustments | Direct student feedback on AI integration effectiveness |
Practice evaluating AI-generated classical conditioning examples. Work through each scenario as you would in class.
| Component | Description | Weight |
|---|---|---|
| Individual Reflection Essay 800 words |
Reflect on: (a) Which theory best explains a behaviour you have personally experienced? (b) How was your understanding shaped by peer discussion? (c) How has your thinking evolved over the lesson? | 30% |
| Collaborative Case Study Analysis Group Product |
Apply learning theories to a complex behaviour (e.g., social media addiction, procrastination). Include: theory-driven explanation, limitations, cultural considerations. | 30% |
| AI Use Transparency Statement AI-Focused |
Document every AI use: prompts, outputs, what was adopted/modified/rejected, and reasoning. Reflect on AI limitations in learning psychology. | 15% |
| Peer Evaluation | Evaluate group members' contributions using a rubric (participation, collaboration quality, conflict resolution). | 10% |
| Reflection on Peer Feedback | Reflect on how peer evaluations and in-class feedback shaped your final portfolio. | 15% |
| Criteria | Excellent (4) | Good (3) | Developing (2) | Beginning (1) |
|---|---|---|---|---|
| Critical Evaluation of AI Outputs | Identifies limitations, biases, gaps with specific examples; clear justification for acceptance/rejection | Identifies some limitations but accepts AI somewhat uncritically | Mentions AI use but limited critical evaluation | No evidence of critical evaluation |
| Independent Judgment | Clearly articulates where human judgment overrode AI, with strong rationale based in psychology knowledge | Shows some independent judgment but occasionally defers to AI uncritically | Rarely demonstrates independent judgment | Indicative of unreflective AI reliance |
| Transparency & Documentation | Thorough, honest documentation of all AI interactions with precise detail | Documents AI use but omits some details | Sparse documentation | Minimal or unclear AI use documentation |
| Criteria | Excellent | Good | Developing |
|---|---|---|---|
| Collaboration Quality | Evidence of distributed expertise; all members contributed; disagreement handled constructively; synthesis of multiple perspectives | Good collaboration but some asymmetry; synthesis present but less integrated | Groupwork evident but product appears assembled from individual sections without true synthesis |
| Critical Synthesis of Theories | Theory applied with depth; limitations addressed; connections between theories explicitly discussed | Theory applied correctly but primarily sequential discussion rather than integrated synthesis | One theory dominates; others absent or superficially addressed |
AI as supplement, never replacement. Instructor explicitly communicates: "AI may help you revise drafts, but the final responsibility for quality, accuracy, and ethics of your work rests with you — and your human educators will always make the final judgment."
| Mechanism | Description | Instructor Oversight |
|---|---|---|
| AI-Generated Draft Feedback Grammarly / LanguageTool |
Students may use AI grammar/style tools on draft essays. Students submit AI-modified drafts AND indicate what they changed based on AI suggestions. | Instructor reviews student AI-change log to assess judgment and independent reasoning |
| Automated Rubric Feedback Gradescope-like tool |
AI provides formative feedback on portfolio sections against rubric criteria (e.g., "Your reflection addresses 'limitations' but doesn't discuss cultural considerations"). | Instructor reviews all AI-generated feedback before releasing to students; adjusts or supplements as needed |
| AI-Assisted Peer Review Turnitin Revision Assistant |
AI flags structural concerns (e.g., "This paragraph lacks a topic sentence"), which students address before instructor final review. | Instructor confirms AI flags are appropriate; provides personalised follow-up where AI feedback was inaccurate |
| AI-Feedback Interrogation Task | After receiving AI-generated feedback, students submit a brief reflection: "Do you agree with the AI's assessment? What might the AI have missed? Where did you override the AI's feedback?" | Used during individual check-ins (office hours) to discuss metacognitive skills |
| Calibration Session (Week 2) | Instructor presents a "mystery draft" where AI feedback flags issues. Class votes on whether the AI feedback was accurate/harsh/misleading. | Instructor facilitates the calibration session, correcting misconceptions about AI feedback |
| ILO | Teaching Activity | Assessment | Pedagogy | AI Integration |
|---|---|---|---|---|
| ILO 1: Analyze classical conditioning | Segment 1: Mini-lecture + AI critique activity; Think-Pair-Share | Group AI critique worksheet; post-test Q1 | Peer discussion labeling AI examples; co-construction | AI examples provide error-rich stimuli for critical analysis |
| ILO 2: Differentiate theories | Segments 1 & 2: Compare AI examples across theories; Bobo Doll extension | Pre-test Q2 & Q4; post-test; portfolio case study | Comparing/contrasting negotiated among peers | AI output imperfect boundaries force differentiation |
| ILO 3: Apply operant conditioning | Segment 2: Operant Design Challenge with AI-generated plans | Portfolio case study; behavior modification plans | Peer critique of AI-generated plans is collaborative task | AI plan critique invites human override and judgment |
| ILO 4: Evaluate strengths/limitations | Segment 3: Ethical critiques of Bobo Doll; media violence discussion | Portfolio case study limitation sections; reflection essay | Discussion-based co-construction of nuanced evaluations | Critique of AI's omission of cultural/ethical nuances |
| ILO 5: Collaboratively synthesize theories | Segment 4: Synthesis poster + 1-min presentations | Peer evaluation; collaborative case study analysis | Direct Social Constructivism — ZPD activity | AI integration required; students co-decide adopt/modify/reject |
| ILO 6: Critically evaluate AI outputs AI Literacy | All segments — AI tasks; AI journal (pre-class); AI reflection (closure) | AI Use Transparency Statement; AI critique worksheets | Instructor models critical AI interrogation; groups practice judgment | Core AI literacy outcome: evaluate accuracy/bias/limitations |
| ILO 7: Reflect on AI ethics AI Ethics | AI-Reflection discussion (closure); academic integrity discussion | AI Use Transparency Statement; exit ticket Q3 | Peer discussion of ethical AI use | Ethical AI integration as intentional curriculum outcome |