Behaviour Analysis: AI & I – Part 2

Curriculum Integration Statement / Values Statement

Value Statement: Creating learning environments where every student, regardless of background, ability, or prior experience, has equitable access to knowledge, support, and opportunities for success. Alignment with GenAI: Integrating AI helps dismantle barriers by giving students individualized explanations, study aids, and alternative ways to engage with content. In terms of impact to the student, the hope is to provide more inclusive support, increased confidence, reduced stigma around help-seeking.

Value Statement: Teaching students to engage with emerging technologies, especially GenAI, ethically, critically, and responsibly, ensuring they develop the judgment needed for academic and professional integrity. Alignment with GenAI: AI is a tool that must be understood, not blindly used. Embedding discussions of data ethics, bias, transparency, and authorship is a must.

Value Statement: Fostering a culture of curiosity, continuous learning, and innovation and adapting to a rapidly changing world. Alignment with GenAI: GenAI encourages exploration, experimentation, and problem-solving. Integrating AI into teaching models supports iterative learning, rapid prototyping, data analysis, and creativity.

Setting the Context/Curriculum Integration Goals

PSY355 is a required course for the General Arts Program (GAP) and a general education course elective in the sciences and social sciences.

Assessment Details

Title: AI & I – Behaviour Analysis (Part 2 of 2)

Note: This activity/assignment/kernel can be paired with another kernel “AI & I: Behaviour Analysis Part 1.” This builds upon the AI literacy skills developed in “AI & I: Behaviour Analysis Part 1,” and focuses on more intermediate AI skills and applications. If an instructor decides to use both kernels, it is recommended to start with “AI & I – Behaviour Analysis Part 1” as the foundational kernel, then assign “AI & I: Behaviour Analysis Part 2” to build upon these skills.

Deliverables/Objectives:

• Students will conduct a behavioral analysis on an AI generated case or by interacting with a pre-trained AI agent.
• Students will collaborate and discuss their findings with their peers.
• Students will use their AI literacy and prompting skills to develop their own case study or pre-trained AI agent that they will share with a peer.
• Students will conduct a second behavioural analysis on their peer generated AI case study/AI agent.
• Students will collaborate together to present their findings/observations/reflections to the class.

Learning Objectives:

• Students will analyze AI-generated emotional response case studies, applying behavioural analysis frameworks covered in prior coursework.
• Students will collaborate with peers and AI tools to compare, debate, and refine their emotional diagnoses, strengthening their communication and teamwork skills.
• Students will practice prompt engineering to create their own AI-generated emotional response cases, fostering creativity and technical proficiency.
• Students will critically evaluate the accuracy and depth of peer and AI diagnoses, reflecting the intersection of human and artificial behavioural analysis.

Materials Required:

• Laptops or tablets with internet access and AI chatbot platforms (e.g., Copilot tools).
• Pre-written AI-generated emotional response case study (provided by instructor).
• Student journals or notes from prior assignments for reference.

Preamble: Personal Emotional Reflection and Journaling

Before assigning this task, the instructor should review the behavioural analysis framework with students and clarify key terms such as emotional response, diagnosis, and other relevant terms. Instructors should also introduce the concept of AI Copilot Agents, explaining how AI systems can be prompted to simulate, mirror, or fabricate emotional responses through prompt engineering and generated scenarios. The expectations and structure of the assignment should be clearly outlined, with emphasis on how this work builds upon students’ previous journal analysis and extends those skills to the evaluation of AI-generated cases. The instructor should further stress the importance of critical thinking, collaboration, and effective communication as students analyze AI-generated content, assess its credibility and implications, and compare it with human perspectives and real-world behavioural interpretations.

Part 1: Instructor-Led Case Study Diagnosis and Peer Collaboration

Present the instructor-created AI-generated emotional response case study, ensuring students understand the context and details of the fabricated scenario.

• Instructor may also show their students the prompts/training they used to create the case study and/or AI agent.

Guide students individually to diagnose the emotional response using established behavioural analysis techniques and referencing their prior work as an anchor. Facilitate one-on-one peer collaboration, where students compare and debate their diagnoses, identifying similarities, differences, and possible misinterpretations. Encourage students to prepare a brief, structured summary of their collaborative findings, focusing on communication, clarity, and evidence-based reasoning.

Part 2: Now it's their turn! - Student-Created AI Emotional Case Studies

Transition to the creative phase by instructing students to use their AI prompting skills to generate a new, original case study featuring a fabricated emotional response. Encourage students to experiment with prompt engineering, adjusting specificity, context, and emotional cues to create nuanced and believable AI responses. Encourage students to document both their prompts and the resulting AI-generated case study for building their prompting skills.

Part 3: Peer Diagnosis and Collaborative Reflection

Have the students pair off and have them interact with their peer created case study/AI agent; they will independently diagnose the emotional traits and behavioural patterns in their peer’s case study/AI agent. Students will then engage in a structured peer-to-peer dialogue where the diagnosing student discusses their findings with the creator, comparing diagnoses with the original prompts and intentions. Encourage students to reflect on the accuracy of their peer’s diagnosis, noting any surprises, misinterpretations, or unique insights. Instruct the students to prepare a concise summary of their collaborative process and discoveries to present to the class at large.

Summary

Students first analyze an instructor-created AI-generated emotional response case study, apply behavioural analysis techniques, and collaborate with a peer to compare diagnoses and present evidence-based findings. Students then create their own AI-generated emotional case study through prompt engineering, and exchange cases with a peer for independent diagnosis and reflective discussion. The overall goal is to strengthen students’ analytical, prompting, communication, and collaboration skills while exploring how AI-generated emotional behaviours can be interpreted and compared.