Teaching – Professional Learning

This cross-sectional study investigates how preservice teachers in the Global South engage with Generative Artificial Intelligence across academic and instructional tasks while navigating infrastructural barriers such as limited internet access and high data costs. The study surveyed 167 preservice teachers from four teacher education institutions in Ghana.

The rapid integration of generative artificial intelligence (AI) in higher education since 2023 has outpaced institutional preparedness, creating a persistent gap between student practices and established ethical standards. This paper draws on mixed-method surveys and a focused literature review to examine student motivations, ethical dilemmas, gendered responses, and institutional readiness for AI adoption.

This report summarizes the outcomes of a two-day international scoping workshop on the role of artificial intelligence (AI) in science education research. As AI rapidly reshapes scientific practice, classroom learning, and research methods, the field faces both new opportunities and significant challenges. The report clarifies key AI concepts to reduce ambiguity and reviews evidence of how AI influences scientific work, teaching practices, and disciplinary learning.

While rapid advances in large language models (LLMs) are reshaping data-driven intelligent education, accurately simulating students remains an important but challenging bottleneck for scalable educational data collection, evaluation, and intervention design. However, current works are limited by scarce real interaction data, costly expert evaluation for realism, and a lack of large-scale, systematic analyses of LLMs ability in simulating students. We address this gap by presenting a three-stage LLM-human collaborative pipeline to automatically generate and filter high-quality student agents.

The rapid integration of Artificial Intelligence (AI) into K-12 STEM education presents transformative opportunities alongside significant ethical challenges. While AI-powered tools such as Intelligent Tutoring Systems (ITS), automated assessments, and predictive analytics enhance personalized learning and operational efficiency, they also risk perpetuating algorithmic bias, eroding student privacy, and exacerbating educational inequities.

As generative AI (genAI) rapidly enters classrooms, accompanied by district-level policy rollouts and industry-led teacher trainings, it is important to rethink the canonical ``adopt and train'' playbook. Decades of educational technology research show that tools promising personalization and access often deepen inequities due to uneven resources, training, and institutional support. Against this backdrop, we conducted semi-structured interviews with 22 teachers from a large U.S. school district that was an early adopter of genAI.

Education in the era of generative AI faces a pivotal transformation. As AI systems reshape professional practices-from software development to creative design-educators must reconsider how to prepare students for a future where humans and machines co-construct knowledge. While tools like ChatGPT and Claude automate tasks and personalize learning, their educational potential depends on how meaningfully they are integrated into learning environments.

Simulating nuanced human social dynamics with Large Language Models (LLMs) remains a significant challenge, particularly in achieving psychological depth and consistent persona behavior crucial for high-fidelity training tools. This paper introduces TACLA (Transactional Analysis Contextual LLM-based Agents), a novel Multi-Agent architecture designed to overcome these limitations. TACLA integrates core principles of Transactional Analysis (TA) by modeling agents as an orchestrated system of distinct Parent, Adult, and Child ego states, each with its own pattern memory.

In the rapidly evolving educational landscape, the integration of technology has shifted from an enhancement to a cornerstone of educational strategy worldwide. This transition is propelled by advancements in digital technology, especially the emergence of artificial intelligence as a crucial tool in learning environments. This research project critically evaluates the impact of three distinct educational settings: traditional educational methods without technological integration, those enhanced by non-AI technology, and those utilising AI-driven technologies.

Large language models (LLMs) based Agents are increasingly pivotal in simulating and understanding complex human systems and interactions. We propose the AI-Agent School (AAS) system, built around a self-evolving mechanism that leverages agents for simulating complex educational dynamics.