AI-Integrated Concept Map Activity

Professor Nirusha Thavarajah is an Associate Professor in the Teaching Stream in Physical and Environmental Sciences at the University of Toronto Scarborough. She holds a Ph.D. in Synthetic Organic Chemistry, an LL.M. in Intellectual Property Law, and an M.Ed. in Higher Education, and teaches chemistry courses including Organic Chemistry and Advanced Bio-Organic Chemistry. Prof. Thavarajah has integrated generative AI into her teaching by using structured exercises where students critically evaluate AI-generated responses, fostering analytical thinking and deeper comprehension of course material. To maintain academic integrity, she has restructured lab assessments by shifting online post-lab reports and quizzes to in-person settings completed during lab time. This approach prioritizes active learning while minimizing reliance on AI for assessments.

Nirusha Thavarajah Associate Professor, Teaching Stream, Department of Physical & Environmental Sciences

Objectives

As part of CHMA10’s (Winter 2026) Experiment 2 (Determining Acetic Acid Content in Vinegar), this assignment supports students to understand complex titration procedures through AI-assisted visualization, while also developing critical AI literacy skills. The assignment strategically integrates Microsoft Copilot as a learning tool for organizing procedural knowledge: a task where AI can genuinely enhance comprehension by transforming linear text into spatial concept maps that reveal relationships between experimental steps, rationales, and calculations.

The required 250-300 word critical reflection ensures students do not simply accept AI outputs, but actively evaluate their accuracy, completeness, and pedagogical value. In addition, by documenting all prompts and refinements, students develop metacognitive awareness about their learning process and AI’s role within it.

A key goal of the assignment is to prepare students for professional environments where they must make ethical decisions about AI use. Students learn to recognize AI’s affordances (efficiency, organization) and limitations (inability to replace critical thinking, domain expertise, or creative problem-solving), while maintaining intellectual ownership of their work.

Process

The activity was designed as a structured assignment combining AI-assisted creation with human-led critical evaluation. The steps included:

Step 1: Review Titration Procedure and Assignment Requirements

Students read Experiment 2 lab manual section on titration procedure
Review assignment rubric clarifying that concept maps must include: purpose, preparation steps, titration technique, endpoint recognition, and calculation pathways
Understand that AI may be used ONLY for concept map creation, NOT for the critical reflection

Step 2: Generate Initial Concept Map with Microsoft Copilot

Students craft initial prompt requesting a concept map for the titration procedure
Example prompt structure: “Create a concept map for a chemistry lab titration procedure that shows the sequence from preparation through calculation, including the purpose and rationale for each step”
Document exact prompt wording in a separate document

Step 3: Iterative Refinement Through Prompt Engineering

Students critically evaluate Copilot’s initial output for accuracy, completeness, and alignment with lab manual
Identify missing elements (e.g., safety considerations, specific volume measurements, indicator color changes)
Craft refined prompts addressing gaps: “Add the specific volumes used in this procedure” or “Include the rationale for each rinsing step”
Document all refinement prompts and note what improved with each iteration
Continue until concept map accurately represents the complete procedure

Step 4: Write Critical Reflection (250-300 words, No AI Permitted)

Students independently answer guiding prompts:
- How effectively did Copilot represent the information?
- What challenges arose when writing prompts?
- What were AI’s strengths and limitations for this task?
- Did the output enhance understanding? Why or why not?
- How could the concept map or creation process improve?
Reflection must demonstrate original thinking about AI’s educational role

Step 5: Compile and Submit

Create single PDF containing: (1) Final concept map, (2) All documented prompts with annotations about what each aimed to achieve, (3) Critical reflection
Submit to Quercus link under Experiment 2 module before lab period begins
Assignment worth 10 points total (5 for concept map quality, 5 for critical reflection depth)

Future-Focused Skill Development

This activity supports future-ready learning by aligning with principles from the University of Calgary’s STRIVE model. For instance, it emphasizes Transparency by explicitly delineating when AI tools can be used (for concept map creation) versus when they are prohibited (during critical reflection writing), while requiring students to document all prompts and refinements to make their AI interaction process completely visible and understandable. It also promotes Responsibility by requiring students to critically evaluate AI outputs for accuracy and completeness, iteratively refine prompts to address gaps, and reflect on AI’s pedagogical strengths and limitations rather than passively accepting generated content, fostering ethical engagement with technology and maintaining intellectual ownership of their learning.

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