Designing GenAI Tools for Personalized Learning Implementation: Theoretical Analysis and Prototype of a Multi-Agent System

Journal of Teacher Education, Год журнала: 2025, Номер unknown

Опубликована: Март 19, 2025

Educator preparation, personalized learning (PL) implementation, and applications of Generative AI converge as three interrelated systems that, when carefully designed, can help achieve the long-sought goal providing inclusive education for all learners. However, realizing this potential comes with challenges resulting from theoretical complexities technological constraints. This article provides a analysis complex interconnectedness among these guided by Cultural-Historical Activity Theory (CHAT). Building on analysis, we introduce CoPL, multi-agent system consisting multiple agents distinct functions that facilitate PL design engage pre-service teachers (PSTs) in dynamic conversations while prompting them to reflect inclusivity agent-generated instructional suggestions. We describe affordances limitations professional tool PSTs develop competencies designing meet diverse needs Finally, discuss future research refining CoPL its practical applications.

Язык: Английский

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Advancing equity and inclusion in educational practices with AI‐powered educational decision support systems (AI‐EDSS)

British Journal of Educational Technology, Год журнала: 2024, Номер 55(5), С. 1974 - 1981

Опубликована: Июль 10, 2024

Abstract A key goal of educational institutions around the world is to provide inclusive, equitable quality education and lifelong learning opportunities for all learners. Achieving this requires contextualized approaches accommodate diverse global values promote that best meet needs goals learners as individuals members different communities. Advances in analytics (LA), natural language processes (NLP), artificial intelligence (AI), especially generative AI technologies, offer potential aid decision making by supporting analytic insights personalized recommendations. However, these technologies also raise serious risks reinforcing or exacerbating existing inequalities; dangers arise from multiple factors including biases represented training datasets, technologies' abilities take autonomous decisions, tool development do not centre concerns historically marginalized groups. To ensure Educational Decision Support Systems (EDSS), particularly AI‐powered ones, are equipped equity, they must be created evaluated holistically, considering their both targeted systemic impacts on learners, Adopting a socio‐technical cultural perspective crucial designing, deploying, evaluating AI‐EDSS truly advance equity inclusion. This editorial introduces contributions five papers special section advancing inclusion practices with AI‐EDSS. These focus (i) review large models (LLMs) applications offers practical guidelines evaluation (ii) techniques mitigate disparities across countries languages LLMs representation educationally relevant knowledge, (iii) implementing intersectionality‐aware machine education, (iv) introducing LA dashboard aims institutional equality, diversity, inclusion, (v) vulnerable student digital well‐being Together, underscore importance an interdisciplinary approach developing utilizing only foster more inclusive landscape worldwide but reveal critical need broader contextualization incorporates questions what kinds decisions being used support, purposes, whose prioritized process.

Язык: Английский

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Personalized learning through AI: Pedagogical approaches and critical insights

Contemporary Educational Technology, Год журнала: 2025, Номер 17(2), С. ep574 - ep574

Опубликована: Март 10, 2025

In this analysis, we review artificial intelligence (AI)-supported personalized learning (PL) systems, with an emphasis on pedagogical approaches and implementation challenges. We searched the Web of Science Scopus databases. After preliminary review, examined 30 publications in detail. ChatGPT machine technologies are among most often utilized tools; studies show that general education language account for majority AI applications field education. Supported by particular stressing student characteristics expectations, results automated feedback systems adaptive content distribution define AI’s educational responsibilities mostly. The study notes major difficulties three areas: technical constraints data privacy concerns; pragmatic barriers. Although curriculum integration teacher preparation considered concerns, challenges come first above technology integration. also underline need thorough professional development activities teachers tools especially targeted instruction. shows efficient application AI-enabled PL requires a comprehensive strategy addressing technological, pedagogical, ethical issues all at once. These help to describe current state provide ideas future developments as well techniques its use.

Язык: Английский

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The Practical Epistemologies of Design and Artificial Intelligence

Science & Education, Год журнала: 2024, Номер unknown

Опубликована: Апрель 18, 2024

Abstract This article explores the epistemological trade-offs that practical and technology design fields make by exploring past philosophical discussions of design, practitioner research, pragmatism. It argues as technologists apply Artificial Intelligence (AI) machine learning (ML) to more domains, brings this same set with it. The basis becomes what it finds. There are correlations between questions designers face in sampling gathering data is rich context, those large-scale faces how approaches context subjectivity within its training data. AI, however, processes enormous amounts produces models can be explored. makes form pragmatic inquiry amenable optimisation. Finally, paper implications for education stem from we AI pedagogy explanation, suggesting availability AI-generated explanations materials may also push directions pragmatism: evidence effective precede explorations why they should be.

Язык: Английский

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Equity in Edtech by Design

Опубликована: Май 1, 2024

This report aims to provide guidance for improving equitable EdTech design, policy and practice. We identified relevant academic literature captured best practices in identifying features, as well biassed design organisational EdTech. Our approach draws from existing indicating that accepted standards indicators have generally proven positively influence developer consumer awareness, policy-makers’ decision-making.

Язык: Английский

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Using Risk-Free Artificial Intelligence in the Classroom

Advances in educational technologies and instructional design book series, Год журнала: 2024, Номер unknown, С. 329 - 362

Опубликована: Дек. 13, 2024

This chapter examines professors' perspectives on using risk-free artificial intelligence (AI) in higher education classrooms, focusing the perceived benefits, challenges, and ethical considerations surrounding AI implementation. Researchers gathered insights into experiences viewpoints integrating educational settings through a qualitative study survey method, semi-structured interviews, questionnaires. The findings reveal that while offers substantial opportunities for enhancing teaching learning, it also brings notable challenges concerns. Based these insights, recommends best practices to ensure responsible effective use of tools education.

Язык: Английский

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The Social Consequences of Language Technologies and Their Underlying Language Ideologies

Lecture notes in computer science, Год журнала: 2024, Номер unknown, С. 271 - 290

Опубликована: Янв. 1, 2024

Язык: Английский

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Ethical AIED and AIED Ethics: Toward Synergy Between AIED Research and Ethical Frameworks

Communications in computer and information science, Год журнала: 2024, Номер unknown, С. 18 - 31

Опубликована: Янв. 1, 2024

Язык: Английский

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A Taxonomy of AI-Based Assessment Educational Technologies

Опубликована: Авг. 13, 2024

Artificial Intelligence (AI) is increasingly applied across various domains, including education, where it enhances numerous aspects of the learning process, from course design to assessment. Despite its benefits in efficiency, scalability, and consistency, AI education different educational stages. This paper focuses on use assessment stage. To that end, this proposes a taxonomy AI-based learner technologies (EduTech) both research industrial perspectives. The provides comprehensive understanding identifies gaps field. Using PRISMA framework, we systematically review related papers tools.

Язык: Английский

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Applications of Artificial Intelligence Techniques in Education

Advances in educational technologies and instructional design book series, Год журнала: 2024, Номер unknown, С. 429 - 450

Опубликована: Ноя. 15, 2024

In the rapidly evolving landscape of education, integration Artificial Intelligence (AI) has emerged as a transformative force, promising to revolutionize way teaching and learning. This book chapter presented applications AI tools in online education these learning environments, interaction between learners instructors significantly influences satisfaction outcomes. Therefore, understanding how impacts this is crucial for identifying potential challenges ensuring safe effective environments. Also, case studies, success stories, benefits education. will be useful educational institutions improve their efficiency with stakeholders learners.

Язык: Английский

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