AI-Resistant Lab Report 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 the first-year course CHMA10: Introductory Chemistry I (Winter 2026), this redesigned lab assessment aimed to ensure authentic student learning by shifting all graded post-lab work from take-home assignments to in-person completion during lab time. This redesign addresses AI-generated homework challenges, while also improving pedagogical practice by mirroring professional laboratory environments, where data analysis occurs immediately after experimentation.

A 4-point pre-lab quiz (open-book, two attempts) encourages thorough preparation, and acknowledges that students may use AI tools in this context to support their learning. In contrast, students are required to complete calculations, discussions, and conclusions during the lab session, with TA support available; this eliminates the traditional two-week gap between data collection and analysis.

Overall, the goal of this redesigned lab report approach is to create assessment conditions that preserve academic integrity, provide immediate feedback, reduce anxiety about post-lab work, and help students develop essential time management and analytical skills.

Process

The activity was designed as a comprehensive restructuring of lab timing and assessment completion. The steps included:

Step 1: Pre-Lab Preparation (Completed 3+ days before lab)

Students access a 4-point pre-lab quiz on Quercus, available 3 days before their scheduled lab period
Quiz is open-book with two attempts, encouraging engagement with lab manual content – students may use GenAI tools to support their learning at this stage
Students complete notebook preparation (Steps 1-8): Purpose, Theory, SDS table, procedure outline, and pre-lab questions (4 points)
TAs check notebook preparation in first 10 minutes of lab; students without completed pre-lab work cannot participate

Step 2: Lab Demonstration and Procedure (First 110 minutes)

First 10 minutes: Check-in and pre-lab verification
Minutes 10-30: TA demonstration of techniques
Minutes 30-120: Students complete experimental procedure in pairs, recording observations and data in notebooks

Step 3: In-Lab Work Period (60 minutes)

Students remain at benches with notebooks and data
Complete all calculations using recorded data with guided prompts from lab manual
Answer discussion questions (4-5 sentences) addressing experimental errors, design flaws, and connections to theory
Write conclusions (3-4 sentences) summarizing findings and experimental error impact
TAs circulate to answer questions and provide immediate feedback

Step 4: Post-Lab Notebook Grading (Final 30 minutes)

TAs evaluate completed calculations, discussions, and conclusions (8 points total)
TAs check workstation cleanliness and safety compliance (2 points)
TAs sign notebooks and provide feedback before students leave
Students receive immediate grade notification, eliminating multi-week waiting periods

Step 5: Cleaning and Sign-Out

Students leave after TA approves notebook completion and workspace cleanliness

Future-Focused Skill Development

This activity supports future-ready learning by aligning with principles from the University of Calgary’s STRIVE model. It emphasizes Transparency by clearly delineating when AI tools can support learning (during open-book pre-lab preparation) versus when students must demonstrate independent analytical skills (during in-lab assessment completion), helping students understand appropriate contexts for AI use. It also promotes Validity by ensuring authentic assessment of student learning through in-person completion of calculations and analysis, mirroring professional laboratory practices where data interpretation occurs immediately after experimentation and validating that students can perform critical thinking independently. Finally, it supports Student-Centred Learning by providing immediate TA feedback during the work period, reducing anxiety through same-day grading, and allowing students to develop time management and analytical skills with scaffolded support rather than struggling alone on delayed take-home assignments. Together, these principles help students build disciplinary competencies, develop responsible AI use practices, and gain confidence in their analytical abilities within authentic scientific contexts.

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