Exploring the impact of ‘choose your own’ assessment styles in large chemistry/biology units in a post AI world.

Stephen George-Williams; Osu Lilje; Henry Matovu; Pierre Naeyaert; Michael  Widjaja; Shane Wilkinson

Authors

Stephen George-Williams The University of Sydney http://orcid.org/0000-0002-2578-1187
Osu Lilje The University of Sydney
Henry Matovu The University of Sydney
Pierre Naeyaert The University of Sydney
Michael Widjaja The University of Sydney
Shane Wilkinson The University of Sydney

Keywords:

Questionnaires, Interviews, Artefact analysis, Creative, Student choice

Abstract

Authentic Assessments have been shown to enhance engagement and skill development, while student choice in assessment format can increase motivation and accountability (Hains-Wesson & le Roux, 2024). However, the integration of AI introduces both opportunities and challenges. While AI may support personalized learning and adaptive feedback, it also raises concerns about academic integrity and the authenticity of student work (Picasso et al., 2024; Stahl et al., 2023). Preliminary data suggest that up to one-third of students may be using AI, but the nature and impact of this use remain unclear.

This study explores the impact of artificial intelligence (AI) on the completion and effectiveness of “choose your own” Authentic Assessments in large first-year science courses. With over 3,000 students enrolled in CHEM1 and BIOL1009, these assessments offer flexible formats—ranging from technical reports to videos and 3D-printed models—designed to foster real-world application, critical thinking, and creativity. The research investigates three core areas:

how students are using AI tools like ChatGPT in these tasks,
how students and staff perceive the experience of completing/marking the assessments, and
whether students are achieving the intended learning outcomes in an AI-influenced academic environment.

References

Hains-Wesson, R., & le Roux, S. (2024). Bridging teacher knowledge and practice: Exploring authentic assessment across educational levels. Education Sciences, 14(8), 894. https://doi.org/10.3390/educsci14080894

Picasso, F., Atenas, J., Havemann, L., & Serbati, A. (2024). Advancing critical data and AI literacies through authentic and real-world assessment design using a data justice approach. Open Praxis, 16(3), 291–310. https://doi.org/10.55982/openpraxis.16.3.667

Stahl, B. C., Antoniou, J., Bhalla, N., Brooks, L., Jansen, P., Lindqvist, B., ... & Wright, D. (2023). A systematic review of artificial intelligence impact assessments. Artificial Intelligence Review, 56, 12799–12831. https://doi.org/10.1007/s10462-023-10420-8