The proposed special issue, “Sustainable Utilization of Educational Data for Learning and Teaching,” focuses on how educational data can be used sustainably and responsibly to facilitate educational practice. It encourages data practices that preserve educational value over time, remain meaningful across contexts, and support informed use by different stakeholders. Here, sustainable utilization refers to the long-term availability of data and their continued value for intelligent support of teaching and learning, in line with the core direction of SDG 4 (“ensure inclusive and equitable quality education and promote lifelong learning opportunities for all”).

The issue is timely as educational institutions increasingly rely on diverse forms of data to inform instruction, learner support, assessment, and educational decision-making. The permeation of AI (and generative AI) into society has made data-driven educational technologies more important to examine not only what can be done with educational data, but how such data use can be made pedagogically meaningful. This direction follows the rapid growth of Large Language Models (LLMs) related educational research (Shi et al., 2025), the increasing attention to explainable AI in educational settings (Khosravi et al., 2022), and recent efforts to co-design data-driven educational technology with practitioners (Ogata et al., 2024).

Educational data now extends beyond conventional learning logs. Current intelligent learning systems increasingly draw on multimodal sources, including textual, behavioral, audiovisual, sensor-based, physiological, and interaction data from assorted learning environments. As Oviatt (2022) notes, multimodal interaction and analytics increasingly incorporate a greater number of information sources, creating both richer opportunities for educational data use and more demanding requirements for data integration, interpretation, and system design. This makes sustainable utilization not only of data access but also of diverse modalities, processed in ways that support reproducibility, transferability, and continued analytical value across diverse educational settings.

Apart from system design and technical infrastructures, a responsible approach to educational data utilization also requires attention to privacy, governance, transparency, fairness, and inclusion. These issues become especially important when educational data is reused over time or across contexts, and when data-informed systems affect learners from diverse cultural and institutional backgrounds. For instance, Ito et al. (2025) discuss risk-based privacy protection, highlighting the tension between preserving analytical value and reducing re-identification risk in data sharing of sensitive educational data. Focusing on recent policy initiatives in Switzerland and Japan, Hangartner et al. (2025) show that data sharing for student-centered education is increasingly tied to new forms of data-based governance that connect classrooms, research, and system-level decision-making. This makes governance, access, and responsible data use central issues in any discussion of sustainable educational data utilization.

By bringing together work from sustainable and responsible educational data use, this special issue aims to advance evidence-based work on how educational data can support teaching and learning in ways that remain meaningful, trustworthy, and reusable over time.

We welcome submissions including, but not limited to, the following topics:

• Empirical studies utilizing sustainable educational data from authentic educational settings to examine how such data inform instructional design, learner support, feedback, or assessment practices.

• AI, generative AI, and learning analytics that facilitate the utilization and interpretation of sustainable educational data in educational practice.

• Studies on the use, integration, or interpretation of multimodal educational data in diverse educational settings.

• Cross-context reuse, longitudinal use, or secondary use of educational data for pedagogically meaningful purposes.

• Ethical, privacy-aware, fair, and inclusive approaches to the collection, management, sharing, and use of sustainable educational data in authentic educational settings.

• Educational technologies, data workflows, or analytic processes that are grounded in sustainable educational data and improve learning and teaching.

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Guest Editors:

Guandong Xu

Education University of Hong Kong

gdxu@eduhk.hk

Oscar Lin

Athabasca University

oscarl@athabascau.ca

Jing Lei

Syracuse University

jlei@syr.edu

Changhao Liang

Kyushu University

bluster3a@gmail.com

Chengjiu Yin (corresponding guest editor)

Kyushu University

yin.chengjiu.247@m.kyushu-u.ac.jp

We invite empirical, methodological and design-oriented studies that identify, explain, and respond to heterogeneous learner profiles in GenAI-supported education, moving beyond designs that treat learners as a single homogeneous group. Relevant settings include STEM, academic writing, literacy, humanities, social sciences, health professions, business, teacher education, vocational education, arts and design, and interdisciplinary learning. We particularly welcome person-centered and process-oriented methods such as latent profile and cluster analysis, growth mixture and latent transition modeling, sequence analysis, process mining, epistemic network analysis, multilevel person-centered modeling, learning analytics, qualitative comparative analysis, case-based designs, and mixed methods. Suitable studies might examine heterogeneous patterns of GenAI use, prompting, feedback uptake, help-seeking, verification, revision, collaboration, self-regulation, metacognitive monitoring, AI literacy, and learning outcomes over time. Work that develops learner typologies, maps engagement pathways, models differential effects, or designs adaptive scaffolds for diverse learners is especially encouraged.

Specifically, this special issue welcomes contributions that address, but are not limited to, the following topics:

• Learner profiles in GenAI-supported education and the differential effects of GenAI across learner groups

• Engagement pathways and longitudinal trajectories of GenAI-supported learning

• GenAI and educational equity, including the reproduction or mitigation of achievement gaps

• GenAI in STEM, humanities and social sciences, and professional and vocational education

• Prompting practices, feedback uptake, and adaptive learning in relation to learner heterogeneity

• Self-regulation and metacognition in GenAI use

• Overreliance, cognitive outsourcing, and productive struggle in GenAI use

• Adaptive scaffolding and pedagogical design for diverse learner groups

• Methodological advances in person-centered GenAI research

• Teacher, peer, and institutional mediation of GenAI use

• Ethical and responsible GenAI use among diverse learners

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Guest Editors:

Michael Yi-Chao JIANG

Shenzhen Technology University

mjiang@sztu.edu.cn

Morris Siu-Yung JONG (corresponding guest editor)

The Chinese University of Hong Kong

mjong@cuhk.edu.hk

Xiaoming ZHAI

University of Georgia

Xiaoming.Zhai@uga.edu

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Educational Technology & Society (ET&S) welcomes special issue proposals on specific themes or topics that address the usage of technology for pedagogical purposes, particularly those reflecting current research trends through in-depth research. 

For more information, please visit the Special Issue Proposals page.

The ET&S Editorial Office