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Introduction: Inquiry-based learning is vital to the engineering design process, and most crucially in the laboratory and hands-on settings. Through the model of inquiry-based design, student teams are able to formulate critical inputs to the design process and develop a stronger and more relevant understanding of theoretical principles and their applications. In the junior-level Biotransport laboratory course at Purdue University’s Weldon School of BME, the curriculum utilizes the engineering design process to guide students through three (3) different modules covering different Biotransport phenomena (diffusivity, mass transport, and heat transfer). Students are required to research, conceptualize, and generate hypotheses around a module prompt. Students design, execute, and analyze their own experimental setups to test the hypotheses within an autodidactic peer-learning structure. Methods: A multi-year study was completed spanning from 2014 to 2016, assessing students’ end of course evaluations. With an integration of the flipped lecture into the lab being first implemented in 2015 (prior to 2015, the flipped lecture was a stand-alone course offered outside of the lab sections), the data presented here offers a comparison of student evaluations between these two course structures. Per the student response rates, the sample size for each year was: n=81 (2016); n=60 (2015); n=48 (2014). The surveys were anonymous and a host of questions related to overall course satisfaction, structure, and content were posed. Results: Analysis of the data showed a consistent increase in overall student satisfaction with the course following the implementation of the new structure. The percent of students giving a satisfactory rating or higher for the 2014, 2015 and 2016 course offerings was 79%, 89%, 92%, respectively. This shows a significant difference between 2014 and 2016. Conclusion: The integration of a flipped lecture into the lab successfully improved student satisfaction and self-perceived understanding of course material. This format also improved the delivery of content to students as assessed by maintaining pertinence to the lab topics and clear understanding of learning concepts.
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This content will become publicly available on August 20, 2026
Exploring generative AI application in general chemistry: A lab-based study of prompt engineering, AI assisted analysis, and critical evaluation of AI generated outputs
This study investigates the integration of generative artificial intelligence (Gen AI) into a General Chemistry II laboratory at St. Bonaventure University to assess its impact on student learning, inquiry, and chemical reasoning. In a redesigned of a common thermodynamics/equilibrium experiment involving cobalt(II) chloride, students used ChatGPT and a retrieval-augmented Gen AI (BonnieChemBot) to assist in calculating equilibrium constants, thermodynamic values, and answering open-ended chemical questions. The lab targeted four key educational goals: (A) mastery of chemical concepts, (B) development of prompt engineering strategies, (C) facilitation of inquiry-driven dialogue with instructors, and (D) critical evaluation of Gen AI outputs by students using chemical intuition. Students attempted all analyses twice: first independently, then with the assistance of Gen AI. Finally, they were asked to reflect on their trust in the Gen AI solutions and generate final answers. Instructor intervention in post-lab analyses was limited to cases where students had clearly attempted the first two steps in this process. While students were encouraged to practice prompt engineering, no grades were assigned to its execution. Instead, grading focused solely on students’ final answers.
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- Award ID(s):
- 2142874
- PAR ID:
- 10652035
- Publisher / Repository:
- American Chemical Society
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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