Presentation Schedule
The Role of Literature Review Assessments in Artificial Intelligence Integrated STEM Preservice Teachers’ Learning Environments (107530)
Session Chair: Maria Tsakeni
Saturday, 11 July 2026 15:25
Session: Session 4
Room: UCL Torrington, B08 (Basement Floor)
Presentation Type:Oral Presentation
Literature review tasks are an assessment-for-learning tool useful for developing mastery of content graduate attributes in STEM preservice teachers. However, the advent of artificial intelligence (AI) has rendered some assessment methods, including literature reviews, less effective for meaningful learning. This is because AI can perform tasks traditionally performed by humans, such as learning and conducting literature reviews. Faced with this challenge, we innovated a literature review assessment in a STEM module taken by 260 primary preservice teachers. The literature review assessment was developed into a design activity in which the preservice teachers engaged in a real-life STEM problem-solving and creative activity, drawing on interdisciplinary knowledge of STEM and sustainable development. The literature review became a sub-step of a project-based assessment guided by design process steps of (1) identifying the problem and conducting background research, (2) designing and planning, (3) prototyping and model creation, (4) presentation and justification, and (5) reflection and collaboration. We used an interpretivist paradigm, a qualitative approach, and a descriptive case study to collect data at each design process step, which were analysed using qualitative content analysis. The authentic learning environments enabled preservice teachers to use both traditional and AI-assisted literature reviews, and the knowledge acquired was applied to problem-solving and creativity. The innovation turned the literature review assessment from a product into a learning process. The study's findings were explained within the framework of the Human-AI collaboration proposed by Jiang et al. (2024) and the constructivist learning theory.
Authors:
Maria Tsakeni, University of the Free State, South Africa
Stephen Chinedu Nwafor, University of the Free State, South Africa
About the Presenter(s)
Dr Maria Tsakeni is an Associate Professor and Head of the Mathematics, Natural Sciences, and Technology Education Department in the Faculty of Education at the University of The Free State in Bloemfontein, South Africa.
Connect on Linkedin
https://www.linkedin.com/in/maria-tsakeni-31318374/
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