Skip to content

Enhancing PISA-aligned argumentation skills of prospective biology teachers through generative artificial intelligence

Oct 2026 · Frontiers in Education · 39 references
Science Education and Pedagogy

Abstract

Despite scientific argumentation being a fundamental competency of the PISA assessment framework, prospective biology teachers often demonstrate insufficient development in evidence-based reasoning and rebuttal construction skills. Considering the limitations of large-scale implementation of individualized approaches in higher education, this study evaluates the effectiveness of Generative Artificial Intelligence (ChatGPT-4) configured as a “Socratic tutor” in developing argumentation skills among future educators. An 8-week quasi-experimental study involving 124 students from a pedagogical university employed traditional peer discussion methods in the control group ( n = 62), while the experimental group ( n = 62) utilized an AI agent that induced cognitive dissonance regarding socio-scientific issues. Analysis of Covariance (ANCOVA) of data obtained through Toulmin's Argument Pattern (TAP) and PISA rubrics confirmed that the experimental group achieved statistically significantly higher overall argumentation quality compared to the control group (F (1,121) = 14.23, p < .001, η 2 = .105). Particularly, participants who received AI-assisted instruction showed marked improvement in forming rebuttals and evaluating counterevidence, which are considered among the most cognitively demanding components within the PISA scientific literacy framework (OECD). These findings suggest that a Generative AI-mediated Socratic instructional package can be an effective and scalable approach for developing epistemic cognition, and they propose a pedagogical model for integrating structured AI-based tutoring into teacher preparation systems aligned with international assessment standards. These findings should be interpreted as evidence for the effectiveness of the structured, AI-mediated Socratic instructional package as a whole, rather than as evidence of AI's inherent superiority over other methods; the relative contribution of the AI medium versus the structured pedagogical protocol it delivers was not directly tested in this design.

Read PDF

Similar papers

#artificial intelligence Open access May 2023

Evaluating the Performance of Large Language Models on GAOKAO Benchmark

GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...

Xiaotian Zhang, Chun-yan Li, Yi Zong et al. · 216 citations · ⚡17

KTO: Model Alignment as Prospect Theoretic Optimization

This work proposes a HALO that directly maximizes the utility of generations instead of maximizing the log-likelihood of preferences, and suggests that the best loss depends on the inductive biases most appropriate for a given setting, an oft-overlooked consideration.

Kawin Ethayarajh, Winnie Xu, Niklas Muennighoff et al. · 195 citations · ⚡28
#artificial intelligence Open access Jul 2024

Gender, Race, and Intersectional Bias in Resume Screening via Language Model Retrieval

This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.

Kyra Wilson, Aylin Caliskan · 131 citations · ⚡8
#artificial intelligence Review Nov 2024

How to Build a Quantum Supercomputer: Scaling from Hundreds to Millions of Qubits

This work shows that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration and introduces high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical...

Masoud Mohseni, Artur Scherer, K. Johnson et al. · 121 citations · ⚡9
#artificial intelligence Review Oct 2025

Ultralytics YOLO Evolution: An Overview of YOLO26, YOLO11, YOLOv8 and YOLOv5 Object Detectors for Computer Vision and Pattern Recognition

This paper presents a comprehensive overview of the Ultralytics YOLO family, emphasizing architectural evolution, benchmarking, deployment, and emerging directions from YOLOv5 through YOLO27, and examines detection, segmentation, depth, classification, pose, oriented detection, tracking, export, quantization, and deplo...

Ranjan Sapkota, Manoj Karkee · 112 citations · ⚡10

Related blog posts

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.