Agentic AI in Higher Education
Agentic AI is the next evolution in AI tools affecting teaching and learning in higher education. Agentic AI is consistently defined as AI that pursues complex, often long-horizon goals with minimal human intervention, adapting plans and actions to evolving contexts. Acharya et al. identify it as a “qualitative leap” (Acharya et al., 2025, pp. 18912) that agents plan, use tools, maintain memory, and self-adjust to changing environments to accomplish multi-step, multi-goal tasks. In contrast to single-task rule engines, agentic AI systems set sub-goals, sequence actions, and coordinate across agents to pursue long-term objectives (Acharya et al., 2025). Similarly, Hosseini and Seilani identify agentic AI as systems which combine autonomy, reactivity, proactivity, and learning ability. They emphasize the field’s shift from “Copilot” (assisted) to “Autopilot” (autonomous) modes and the importance of hierarchical agent structures to coordinate complex work (Hosseini & Seilani, 2025).
In educational terms, Agentic AI is independent, dynamic, responsive, and adaptive (Raidas & Bhandari, 2025). This article looks ahead to this next step, anticipating some implications to provide suggestions for students, instructors, and institutions, while addressing some critical challenges of integration, ethics, and governance.
The Future for Students: Personalized Learning Pathways
For students, the emergence of Agentic AI most prominently promises a transition from one-size-fits-all education toward an individualized learning journey. The most predictable application will be in the evolution of intelligent tutoring systems. These will not be simple Q&A tools but agents that monitor student progress over an entire course or program and respond automatically to emergent student needs. These tutoring systems will identify misconceptions, provide adaptive feedback without constant human prompting, and personalize the pacing of instruction (Acharya et al., 2025; Raidas & Bhandari, 2025). Sargsyan (2025) examines this in the context of language education. For example, scenario-based learning platforms such as Duolingo or Khanmigo provide real-time scaffolding and tailored feedback based on users’ performance. This means that students can receive timely, personalized tutoring as they master foundational knowledge and progress through learning tasks at their own optimal pace.
Although the primary benefit for students is the ability to personalize learning on an unprecedented scale, this support comes with significant risks that must be proactively managed. A major concern, as highlighted by Sargsyan (2025), is that over-automation may lessen students’ metacognitive engagement, undermining their self-regulated learning and reflective learning practices. Learners become passive recipients of knowledge, and they tend to use AI for rote memorization or superficial analysis. This suggests that, from the perspective of AI users, students need to be cautious of cognitive offloading when interacting with AI, evaluate AI responses, critically engage with AI, and develop critical higher-order skills as well.
The Future for Instructors: Augmented Teaching Experience
A recurring and crucial theme in the literature is that Agentic AI should be understood as a tool for augmenting teaching, not replacing instructors. Agentic AI in education is expected to primarily automate procedural and administrative tasks such as attendance taking, grading for completion, and answering student’s objective syllabus questions. Acharya et al. (2025) argue that Agentic AI is best deployed to relieve educators of administrative burdens, thereby enabling them to dedicate more time to pedagogical creativity, mentorship, and direct student engagement. The integration of planning and memory capabilities of Agentic AI allows instructors to tailor their pedagogical instruction dynamically based on both course goals and student progress (Acharya et al., 2025).
In the role of teaching assistants, arguably the most immediate use institutions are likely to employ, Agentic AI systems can answer routine student questions, retrieve learning materials, track learner progress, and escalate unresolved cases to human instructors (Acharya et al., 2025; Hosseini & Seilani, 2025). Agentic AI also helps to assemble lecture materials, author examples, and contextualize readings to course objectives, such as generating practice exercises and contextualized examples. Acharya et al. (2025) note that the integration of planning and memory capabilities allows agents to tailor such materials to course objectives and student progress, as well as individual student interest and aptitude.
In assessment, agentic AI is applied to grading and feedback, though this use and its value remains contested. Raidas and Bhandari (2025) identify concerns that automated grading systems may inadvertently introduce bias, particularly against non-native English speakers in algorithmic scoring when the training data are insufficiently representative. Sargsyan (2025) advocates for an insistence on learning design that explicitly fosters self-regulated learning by including critical metacognitive prompts and cautions that passive use of automatic grading can harm student learning outcomes by eroding student metacognitive development.
The potential for Agentic AI automation prompts questions about potential reconceptualization of the instructor's role. Hosseini and Seilani (2025) frame the shift from using AI as human assistant toward using AI without the need for human oversight. This is the next evolutionary step, and the collaboration of humans with AI raises questions of redefining human roles. In the field of education, repetitive tasks can be automated through a series of directions to an Agentic AI. Instructors can focus on facilitating conceptual discussions, nurturing critical thinking, and providing ethical judgment that AI lacks. However, this potentially useful outcome of Agentic AI is not guaranteed. Sargsyan (2025) cautions strongly about the importance of maintaining teacher agency as a means of ensuring integrity in the teaching and learning dynamic. Without institutional frameworks for AI literacy and governance, instructors risk being sidelined from critical pedagogical decisions. Raidas and Bhandari (2025) reinforce this with real-life cases showing that overreliance on AI introduces harms to learners such as insufficient real-time adaptability of educational content, ignoring emotional states and well-being, lacking guidance on customized learning trajectories. Practiced human instructors are supposed to address these issues. The collective literature suggests that Agentic AI must be designed to enhance the educator's role, with the instructor remaining central.
The Future for Institutions: Integration, Ethics, and Governance
Much of the current research and prevailing opinions on this topic suggest that the full and responsible integration of agentic AI into higher education should ultimately be planned and lead at an institutional level. Successful integration would require moving beyond isolated applications to a strategic, responsive, campus-wide approach defined by complex, principled ethical frameworks and managed change strategies.
Looking ahead, Hosseini and Seilani (2025) highlight the potential for hierarchical multi-agent systems to coordinate course operations, in which different agents manage different tasks including lesson planning, assessment generation, and predictive risk detection. Acharya et al. (2025) extend this idea by noting the promise of memory-rich tutoring agents to personalize instruction over extended timeframes while maintaining transparency through memory traces. Meanwhile, Joshi (2025) adds a programmatic perspective, arguing that agentic AI can help redesign curricula by aligning course content with workforce demands and ensuring that higher education remains responsive to broader social and economic responsibilities, for example, AI-integrated teaching methods improving graduate employability rates, expanding AI-driven entrepreneurship programs to create new job opportunities.
Again, this systemic integration brings critical challenges of transparency, bias, and integrity. Transparency is a foundational requirement for trust and critical inquiry into the efficacy of the tools employed. Acharya et al. (2025) stress the importance of maintaining inspectable memory traces, enabling educators and students to understand and critically examine the rationale behind AI outputs. Instructors are encouraged to clarify how AI is used in their course and inform students of the expectations for mutual trust and academic integrity. Sargsyan (2025) and Raidas & Bhandari (2025) emphasize governance, particularly around data privacy, fairness, and accountability. These authors point to risks in unregulated data collection and call for strong institutional frameworks to ensure ethical use. They also mention the research gap in this understudied issue and call for more empirical studies on ethical consideration of agentic AI in the context of higher education.
Bias and privacy represent another major concern that institutions must address. Raidas and Bhandari (2025) demonstrate how algorithmic grading systems can reproduce and amplify existing inequities, particularly disadvantaging marginalized groups when training data is inadequate. This significant limitation underscores the urgent need for diverse datasets and careful human oversight. Privacy and security are also recurrent concerns, as both Raidas and Bhandari (2025) and Sargsyan (2025) warn against the unregulated collection and use of sensitive student data. Joshi (2025) further underscores the lack of long-term evidence on the impact of agentic AI on student learning outcomes, especially in light of these risks, suggesting that the field remains in an emerging stage with significant research gaps.
Furthermore, academic integrity arises as a pressing issue, as the content-generation capabilities of Agentic AI blur lines of authorship. Sargsyan (2025) and Acharya et al. (2025) argue for clear institutional policies and redesigned assessments that evaluate the learning process as well as the final product. Hosseini and Seilani (2025) propose comprehensive enterprise frameworks for AI adoption that begin with needs assessment and risk analysis and extend to professional development and workforce training, thereby preparing the next generation to effectively implement AI.
Therefore, the pivotal role of institutions is threefold. First, they must establish clear, accessible guidelines that define the acceptable and ethical use of Agentic AI in teaching and learning, providing a concrete framework for instructors and students. Given clear institutional policies, instructors can then employ a useful set of principles to help them navigate ethical considerations, academic integrity and practical applications of AI in their classrooms. Second, institutions must invest in ongoing professional development through emerging tool acquisition, workshops, and training sessions to equip educators with the necessary skills and knowledge. Finally, recognizing the rapid pace of change, institutions must commit to regularly reviewing and updating these guidelines and training opportunities, ensuring the academic community is supported with the most current and relevant information to innovate responsibly while safeguarding educational objectives.
Conclusion
The emergence of Agentic AI marks a pivotal moment for higher education, suggesting a transition from task-level automation to the creation of intelligent, collaborative ecosystems. The literature consistently points to a future where these systems take on greater operational complexity, from memory-rich tutors that personalize learning over extended time (Acharya et al., 2025) to cross-functional campus agents that coordinate teaching, advising, and administration under transparent governance structures (Joshi, 2025; Raidas & Bhandari, 2025). The main theme of the reviewed literature is that the next steps are not reliant solely on technological advancements but will rely profoundly on human and institutional change. Positive advancement through any use of agentic AI in higher education requires the development of AI literacy, ethical structures, and training for educators (Sargsyan, 2025). The ultimate outcome of successful Agentic AI integration will not be the replacement of human teachers by machines, but the careful evolution of reliable, multi-agent ecosystems that embed AI into higher education in the service of student success.
References
Acharya, D. B., Kuppan, K., & Divya, B. (2025). Agentic ai: Autonomous intelligence for complex goals–a comprehensive survey. IEEe Access.
Hosseini, S., & Seilani, H. (2025). The role of agentic ai in shaping a smart future: A systematic review. Array, 100399.
Joshi, S. (2025). The transformative role of agentic GenAI in shaping workforce development and education in the US. Available at SSRN 5133376.
Raidas, M. A., & Bhandari, R. Agentic ai in education: redefining learning for the digital era. Artificial Intelligence in, 89.
Sargsyan, L. (2025). Integrating agentic ai in higher education: balancing opportunities, challenges, and ethical imperatives. Foreign Languages in Higher Education, 29(1 (38)), 87-100.