Educating for Human Agency in the Agentic Era
Fajar Persada Supandi,[1] Muhammad Yunus Maulana,[2] Niken Resminingpuri Krisdianti,[3] Muhammad Salman Abidin.[4]
DOI: 10.17613/w2217-f2223
Abstract
The emergence of agentic artificial intelligence (AI) is changing the relationship between humans and intelligent technologies in education. Unlike generative AI, which primarily responds to user instructions, AI agents can plan tasks, use digital tools, and perform multi-step activities with a degree of autonomy. This development requires education to reconsider its priorities. This editorial argues that the central educational challenge in the agentic era is not simply improving students’ ability to use AI, but strengthening their capacity to exercise human agency. Education needs to develop learners who can define goals, direct AI systems, evaluate their outputs, make informed judgments, and remain accountable for their decisions. This shift has implications for AI literacy, independent thinking, assessment, disciplinary knowledge, the role of teachers, academic responsibility, and educational equity. As AI becomes increasingly capable of performing cognitive and operational tasks, education should place greater emphasis on capabilities that support meaningful human decision-making. The agentic era therefore calls for an educational approach in which AI serves as an instrument for human development while human beings retain responsibility for the purposes, boundaries, and consequences of its use.
Keywords:
agentic artificial intelligence; human agency; education; AI literacy; educational assessment
JEL Classification:
I20 (General Education); I21 (Analysis of Education); O33 (Technological Change: Choices and Consequences; Diffusion Processes); J24 (Human Capital; Skills; Occupational Choice; Labor Productivity); I24 (Education and Inequality)
Editorial
Artificial intelligence is entering a new phase in education. Generative AI has already changed how students and teachers create and access information (Lee et al., 2024). Now, AI agents are beginning to do more. They can plan tasks, use digital tools, complete several steps, and act toward a defined goal with limited human intervention (Hosseini & Seilani, 2025). This development raises a more fundamental question for education: when machines can increasingly act on our behalf, what should humans continue to learn and do for themselves?
The answer begins with human agency. Human agency is the capacity to define goals, make decisions, exercise judgment, and take responsibility for actions (Bandura, 2006). AI agents may have the capacity to act, but they do not determine the human purposes behind those actions (Popa, 2021). Education should therefore focus not only on increasing students' ability to use AI, but also on strengthening their ability to decide when, why, and how AI should be used (Luckin, 2025).
This requires a broader understanding of AI literacy. Students need to learn how to direct AI agents rather than simply prompt them (Biswas et al., 2005). They should be able to define a problem, break it into tasks, delegate appropriate activities, establish limits, monitor the process, and evaluate the outcome (Ozmen Garibay et al., 2023). These skills will become increasingly important as students move from interacting with AI systems to managing systems that can act with greater autonomy (Alm, 2024).
Judgment is at the center of this process. AI agents can produce information and recommendations quickly, but speed does not guarantee accuracy or appropriateness (chander et al., 2025). An agent may use weak evidence, misunderstand a context, or produce a confident answer to a poorly defined problem (Williamson & Prybutok, 2024). Students therefore need to question AI outputs, check evidence, identify assumptions, and recognize uncertainty (Bearman & Ajjawi, 2023). The ability to evaluate an answer is becoming as important as the ability to produce one (Cope et al., 2021).
Education must also protect students' capacity to think independently. AI can reduce the time required to perform many cognitive tasks, but excessive reliance on it may weaken the learning process itself (Jose et al., 2025). Students need opportunities to solve problems, make mistakes, revise their ideas, and develop understanding without always delegating difficult thinking to a machine (Lieberman & Linn, 1991). The goal should be to use AI to extend human capability while preserving the intellectual capacities that make meaningful human agency possible (Yan et al., 2024).
This issue has direct consequences for assessment. If AI agents can produce essays, presentations, code, and analyses, the final product alone provides less evidence of what a student actually understands (Pallant et al., 2026). Assessment should therefore give greater attention to reasoning and process. Oral defense, reflection, project development, problem-solving, and explanations of methodological choices can help reveal whether students understand and can justify their work (Jonassen & Kim, 2010). The central question should not simply be whether AI was used, but whether the student remains intellectually responsible for the result.
The importance of disciplinary knowledge also remains. AI does not remove the need to understand a subject (Wang et al., 2019). A student cannot reliably evaluate an AI-generated historical analysis without knowledge of history (Kindenberg, 2024). A researcher cannot assess an AI-generated literature review without understanding research methods and scholarly evidence (Tingelhoff et al., 2025). In the agentic era, foundational knowledge provides the basis for supervising and evaluating AI rather than becoming less relevant.
These changes also affect teachers. As AI systems become better at providing information and performing routine tasks, teachers can place greater emphasis on developing students' judgment, inquiry, reflection, and ethical reasoning (Meletiou-Mavrotheris et al., 2025). Teachers can also help students understand when delegation to AI is appropriate and when direct human engagement is necessary (Luo et al., 2026). Their role is therefore not simply to introduce AI into the classroom, but to design learning environments in which technology supports human development (Carvalho et al., 2022).
Accountability must remain central. When a student delegates a task to an AI agent, responsibility for the outcome does not disappear (Köbis et al., 2021). The same applies to researchers, professionals, and educational institutions. AI may perform an action, but humans remain responsible for deciding whether that action was appropriate and for dealing with its consequences (Coeckelbergh, 2023). Education should therefore teach the principle of responsible delegation: AI can perform a task, but humans must remain accountable for the purpose, boundaries, and consequences of that task (Ateşkan, 2026).
There is also an important question of educational equity. Access to advanced AI agents will not be equal. Differences in devices, connectivity, software access, institutional support, and AI skills may create new forms of educational inequality (Bulathwela et al., 2024). Institutions therefore need to consider not only how to adopt agentic AI, but also how to ensure that its benefits are available to students across different social and educational contexts (Jin et al., 2025).
The agentic era ultimately requires education to reconsider its priorities. Education is not only about producing information or completing tasks efficiently (Barr & Tagg, 1995). It develops people who can think, judge, create, communicate, cooperate, and act responsibly. These purposes remain important even when machines can perform more tasks (Binkley et al., 2012).
The central challenge, therefore, is not whether AI should become more capable. It is whether humans will remain capable of deciding what those capabilities should be used for. As AI agents gain greater capacity to act, education must place greater emphasis on the human capacity to choose goals, exercise judgment, and accept responsibility. Educating for human agency in the agentic era means preparing learners not merely to work with intelligent machines, but to remain responsible for the purposes that guide them.
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