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

This special issue aims to advance our understanding of what learning should look like, what core learning processes or skills should students maintain or onload, and what can be offloaded to AI in the era when generative AI (Gen AI), including large language models (LLMs), are pervasive in the education contexts. A growing number of opinion articles and empirical studies have documented the pros (e.g., providing immediate feedback, supporting personalized learning) and cons (e.g., leading to overreliance, cognitive and metacognitive laziness, and plagiarism) of using Gen AI for learning. More specifically, in the domain of cognition, recent studies have begun to examine the roles of critical thinking (Hou, Zhu, & Sudarshan, 2025), higher-order thinking (Lu et al., 2024), and self-regulated learning (Xia et al., 2026) in shaping learners’ learning processes involving Gen AI, as well as the emergence of cognitive laziness (Fan et al., 2024), overreliance on Gen AI (Hou, Zhu, Sudarshan, Lim, et al., 2025), and cognitive debt (Kosmyna et al., 2025) prompted by its use. For instance, using electroencephalography, Kosmyna et al. (2025) found that participants who wrote their essays with the assistance of LLMs had the weakest neural connectivity compared to their counterparts who used brains only or used search engines.

Despite these developments, there remains a lack of systematic conceptual frameworks and discourse on future learning in which human–AI hybrid intelligence is inevitable, and how epistemic and regulatory responsibilities should be distributed between humans and AI across learning contexts.

The roles attributed to AI in learning environments reflect underlying epistemological assumptions. For example, designers of intelligent tutoring systems often treat knowledge as transmissible information, positioning AI as a tutor delivering sequenced instruction (VanLehn, 2011). In contrast, proponents of social constructivism conceive learning as dialogic and meaning making, leading to the development of AI learning companions or thinking partners (Mossbridge, 2024). From a knowledge-building/creation perspective, learners are positioned as epistemic agents who construct new knowledge, with AI supporting collaborative knowledge creation (Chen & Zhu, 2023; Scardamalia & Bereiter, 2006). Taken together, these theoretical differences underscore the need for a nuanced account of what cognitive work can be offloaded to AI and what should remain, or even be deliberately onloaded, within learners’ cognition.

This special issue welcomes submissions of empirical studies (e.g., experiments, design-based research, learning analytics, multimodal data), theoretical and conceptual papers, systematic reviews, meta-analyses, and position papers that discuss future learning through human-AI hybrid intelligence. Manuscripts should be grounded in clearly articulated epistemological or theoretical perspectives. Topics of interest include, but are not limited to the following:

• Theories and conceptual frameworks for human–AI hybrid intelligence in future learning

• LLM-enabled learning analytics and educational systems that support teaching and learning

• Instructional and design interventions that promote responsible, effective, and developmentally sound use of GenAI for cognition

• Long-term impacts of GenAI on learners’ thinking and/or cognitive/metacognitive development

• Impacts of GenAI on teachers’ instructional design, implementation and reflections

• Future-oriented perspectives on the co-existence of human and AI.

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Guest Editors:

Gaoxia Zhu (corresponding guest editor)

Nanyang Technological University

gaoxia.zhu@nie.edu.sg

Bodong Chen

University of Pennsylvania

cbd@upenn.edu

Wenli Chen

Nanyang Technological University

wenli.chen@nie.edu.sg

Yuqin Yang

Central China Normal University

yangyuqin@ccnu.edu.cn

Jim Slotta

University of Toronto

jslotta@gmail.com

Yew Soon Ong

Nanyang Technological University

ASYSOng@ntu.edu.sg

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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