How Can AI Agents Support Students and Instructors?

AI agents can move beyond single-turn question answering by planning, using tools, remembering relevant context, coordinating subtasks, and adapting support over a sequence of interactions. For students, plausible roles include adaptive tutoring, study planning, formative feedback, guided problem solving, practice generation, simulation, prerequisite recommendations, and reflective or metacognitive prompting. For instructors, agents can assist with material development, question generation and validation, feedback triage, course analytics, instructional-design workflows, resource retrieval, and the orchestration of specialized agents.

Recurring agentic capabilities

The article Agentic Workflows in Education describes four recurring agentic capabilities: reflection, planning, tool use, and multi-agent collaboration. Each adds possibilities but also introduces interpretability, coordination, trust, latency, and oversight challenges.

What AI agents can do well (positive implications)

• Sustained, adaptive support. Unlike single-turn chatbots, agents can maintain a learning conversation over many turns — remembering what a learner knows, adapting difficulty, and sequencing multi-step Scaffolding. This supports adaptive and personalized learning at scale.
• Unburdening instructors. Agents can draft materials, generate and validate questions (e.g., a generator + validator pairing), triage feedback, and orchestrate specialized sub-agents, freeing teachers for higher-value interaction.
• Rich interaction and productive friction. Multi-agent classrooms and simulated peers create varied dynamics — peer-like discourse, constructive disagreement, role-play — that support collaborative learning and Socratic-style probing. Agents designed to challenge rather than agree can push learners toward deeper reconsideration (constructive-conflict agents improved design outcomes in research).
• Low-risk practice and simulation. Agent-based simulations (simulated students, clinical scenarios) let learners practice in safe, repeatable environments before real-world application.

Key risks and caveats (negative implications)

• Over-automation can hollow out learning. The more an agent automates, the less cognitive work the learner does. Proactive agents can leave students as passive consumers, weakening the effortful processes that build durable learning and raising over-reliance risk.
• Reduced metacognitive engagement. If agents handle planning and monitoring, learners may not develop the Metacognition and self-regulation that education aims to build. Agents should elicit, not replace, these processes.
• Misplaced trust and verification gaps. Autonomous agents can produce plausible but unvalidated output; learners and teachers may over-trust it. Robust verification and AI Literacy become more important as agents gain autonomy.
• Opacity and accountability. Multi-agent systems complicate human oversight — which agent is accountable for an error, and where does a human intervene? Coordination failures and persona drift can undermine reliability and Pedagogical Safety.
• Equity and bias. Agents can reproduce training-data bias at scale, and unequal access to capable agentic systems can widen inequity.

What the evidence shows so far

Concrete results — and cautions — are accumulating. Tutor CoPilot — the first randomized controlled trial of a human-AI system in live tutoring — gave novice tutors real-time expert-like guidance drawn from experienced tutors' reasoning: across 900 tutors and ~1,800 students, students of tutors with access were 4 percentage points more likely to master topics, rising to 9 p.p. among the lowest-rated tutors, whose students caught up to those of higher-rated tutors in the control group. It cost about $20 per tutor annually, and analysis of 550,000+ tutoring messages showed tutors shifting toward asking guiding questions rather than giving away answers — a clear case of AI augmenting the teacher rather than replacing them.

But "agentic" is not automatically better. A study with 130 students in an e-commerce course found both Generative AI chatbots and GAI agents rated above traditional e-learning on learning enhancement and personalization — yet no statistically significant difference between the chatbot and the agent conditions, so added autonomy did not translate into added learning value. The proposed Agentic GAI-Supported Learning Framework accordingly treats agents as bounded, human-supervised learning partners, with goals, checkpoints and final decisions reserved for humans.

Two further cautions matter. First, withdrawal: randomized trials show that even brief AI assistance can depress subsequent unassisted performance, and the post-withdrawal interval — named cognitive washout — is almost entirely unmeasured, so the durability of agent-assisted learning gains is unknown. Second, evaluation: deploying eight AI teaching agents across a medical curriculum, platform-generated scores ranked agents differently from an independent expert rubric (the platform's third-ranked agent came last on teaching quality) because platform scores index student performance rather than agent teaching quality. The AI Governance review Beyond the Agent Label adds a proportionality rule — autonomy should not exceed the maturity of the evidence or the strength of accountable human control — noting that evidence is strongest for artifact-level outcomes and weakest for durable learning and equity. For how to build one, see What Are Best Practices for Developing an Effective AI Tutor?; for how to evaluate whether it works, see What Measures and Research Methods Can an Instructor Use to Evaluate AI-Related Interventions?.

The state of the evidence

The evidence base is still emerging. The knowledge base's Agentic AI in Education synthesis draws on a scoping review of 474 studies but notes substantial concentration in higher education, STEM, short-term designs, and text-based tutoring; only a minority of the reviewed work explicitly grounded its systems in educational theory, and rigorous long-term classroom validation remains limited.

The key design warning is therefore not to equate greater autonomy with better learning. Agentic AI and Pedagogical Best Practice recommends intentional friction, dynamic scaffolding, and human oversight so that agent initiative does not remove the learner's own planning, monitoring, judgment, and effort. See also Intelligent Tutoring and Human-in-the-Loop AI.