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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