From Debate to Design: Reflections on AI, Cognition, and Learning at AERA 2026
Authors: Yerin Seung; info@ciddl.org
Attending the 2026 American Educational Research Association (AERA) Annual Meeting in Los Angeles, I was struck by how much the conversation around AI in education has evolved. Across sessions, scholars from diverse disciplines engaged in rich discussions about integrating AI into teaching and learning. Compared to last year, the tone felt notably more mature. Rather than questioning whether AI should be used in education at all, there was a growing recognition that AI is already embedded in daily life and learning environments. The central question is no longer whether to use AI, but how to use it well.
Designing for Balanced AI Integration
A clear shift this year was the move away from polarized conversations that framed AI as either a transformative opportunity or a significant threat, toward more nuanced discussions about balance, with a greater focus on the design of AI-supported learning. One idea that consistently surfaced was that AI itself does not inherently produce positive or negative learning outcomes. Instead, its impact depends on how it is used, specifically, the interaction among the tool, the instructional approach, and the learner’s cognitive processes. This perspective echoes long-standing media debate, emphasizing that tools alone do not determine learning. As a result, researchers are increasingly focused on how to integrate AI intentionally in educational settings. The goal is not simply to adopt AI but to design learning experiences in which AI enhances thinking rather than replaces it. This shift reflects a broader recognition that meaningful AI integration requires careful attention to how students engage cognitively with both the tool and the task.
What the Research Focuses On: Cognition, Motivation, and AI Use
The empirical studies presented at AERA reflected this growing emphasis on intentional design. Many sessions examined how students interact with AI systems and what patterns of use are associated with deeper, more meaningful learning. A recurring focus was on cognitive engagement, how AI can take over certain aspects of thinking, and how this influences students’ decision-making processes and learning outcomes.
Researchers also explored the role of metacognition in AI use. For example, some studies analyzed how prompting, reflection, and iterative engagement with AI tools can support students in thinking more deeply about their learning. They also examined students’ chat histories and reflective responses to understand better how different types of AI use contribute to or hinder learning. These studies employed a range of methodologies, including quantitative, qualitative, and mixed methods approaches, as well as advanced techniques such as multimodal learning analytics and epistemic network analysis.
Integration of Theoretical and Conceptual Framework
Many of these studies were grounded in established theoretical frameworks, such as Bloom's Taxonomy, to guide both instructional design and analysis. At the same time, some sessions drew on the concept of extended cognition, highlighting how technologies have long shaped the way people think and work. From this perspective, technology is not inherently detrimental. However, generative AI introduces a new level of complexity, as it can perform not only lower-level tasks but also higher-level cognitive processes.
This raises an important question for educators: if AI can take on more of the thinking, what should students still be expected to do themselves? Several sessions suggested that this makes it even more critical to support the development of foundational capacities, including executive and metacognitive skills. These capacities enable students to use AI strategically, engaging with it in ways that support, rather than diminish, their own thinking and learning.
The Missing Piece: Learner Variability in AI Research
Despite these important advances, one gap was consistently noticeable across sessions: the limited attention to learner variability. While sessions acknowledged that learning outcomes depend on the interaction between tools, instructional methods, and cognitive processes, relatively few explicitly examined how these dynamics differ across learners. This omission is particularly significant given what we know from special education. Students vary widely in their cognitive profiles, prior knowledge, motivation, and need for support. As a result, the effectiveness of AI-supported learning is unlikely to be uniform across all students. Yet much of the current research still implicitly assumes a more generalized learner.
This raises a critical question that remains underexplored: for whom does AI integration work, and under what conditions? This question is especially important for students with disabilities and other diverse learning needs. For some learners, AI may provide valuable scaffolds that increase access and reduce barriers. For others, it may lead to overreliance or reduce opportunities to develop essential skills. As the field continues to evolve, it will be increasingly important to move beyond one-size-fits-all approaches and more explicitly consider learner variability. Doing so will help ensure that AI integration supports not only learning in general, but learning for specific students in specific contexts.
Final Thoughts: Toward More Intentional AI Integration
Overall, AERA 2026 reflected a field that is moving in a promising direction. The conversation is shifting from broad debates to more focused discussions about design, from general attitudes toward AI to deeper examinations of how AI shapes learning processes, and from speculation to empirical investigation. At the same time, the next step is clear. To fully understand the role of AI in education, we need to ask more precise questions about how it interacts with different learners and learning contexts. This includes examining how AI influences thinking, what kinds of supports enable productive use, and how instructional design can guide students toward meaningful engagement. As researchers and educators, this moment presents both an opportunity and a responsibility. By continuing to refine how we study and design AI-supported learning, we can help ensure that these tools are used not just efficiently, but thoughtfully, in ways that genuinely support students’ thinking and learning.
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