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Agentic AI

created 2026-08-01 · updated 2026-09-26

Agentic AI in Education — AI systems that autonomously plan, execute, and adapt multi-step workflows to achieve learning goals, going beyond single-turn Q&A to act as persistent, goal-directed collaborators: AI tutors that scaffold over extended interactions, multi-agent systems that orchestrate instructional designs, and agents that co-regulate learning. This paradigm shift from a prompt-responding tool to an active collaborator carries both promise and risk: agentic AI can personalize and deepen learning, but it also threatens Learner Agency, cognitive effort, and control. The knowledge base's scoping review, tool-invariant framework, and pedagogical best-practice articles examine this tension.

Questions to Consider

  • Agentic AI doesn't just answer questions — it plans, executes, and adapts multi-step workflows toward a goal, acting as a persistent collaborator. How is learning with a proactive agent different from learning with a tool you have to prompt?
  • The field draws a line between "conversational" and "agentic" AI (a system qualifies only if it meets several criteria such as planning, memory, autonomy, and goal-directed action). Where is that line in practice — and does calling every chatbot "agentic" hide more than it reveals?
  • The more an agent automates, the less cognitive work the learner does. Where is the line between an AI that scaffolds your learning and one that does your learning for you?
  • A scoping review found only 29% of agentic AI studies grounded their systems in educational theory. If most systems aren't theory-based, what should make you skeptical when evaluating an 'intelligent' tutoring agent?
  • Multi-agent systems orchestrate specialized agents with distinct roles. When several agents work together in a classroom, who is accountable — and where should a human intervene?
  • The field's central tension is personalization versus learner agency and cognitive effort. If a tutor becomes so good at adapting that you never have to struggle, what learning are you actually getting?
  • Where on the Copilot-to-Autopilot spectrum should a given task sit — and does moving a task from "agent proposes, learner disposes" to "agent owns it" ever serve learning rather than just efficiency?
  • Hybrid agents grounded in established design theory outperformed pure prompting. Why might a theoretically-grounded system beat raw prompt-engineering — and what does that say about how an agent's 'smartness' is measured?

Introduction

Agentic AI refers to artificial intelligence systems that can autonomously plan, execute, and adapt multi-step workflows to achieve learning goals — going beyond single-turn question-answering to act as persistent, goal-directed collaborators in educational contexts. In education, agentic AI manifests as AI tutors that scaffold learning over extended interactions, multi-agent systems that orchestrate complex instructional designs, and autonomous agents that adapt their pedagogical strategies based on learner needs. This emerging paradigm shifts AI from a tool that responds to prompts to a collaborator that actively guides, adapts, and co-regulates learning processes.

Defining and classifying agentic AI

Kostopoulos et al. (2025) supply an operational definition the field otherwise lacks. They propose a six-criteria checklist — a system counts as agentic if it meets at least four: autonomy (action independent of continuous human intervention), reasoning/planning, memory/context-awareness, goal-directed action toward learning outcomes, adaptability, and dynamic collaboration/initiative. The ≥4 threshold deliberately excludes reactive chatbots (a static FAQ bot without planning or persistence does not qualify) while accommodating diverse architectures. They also organize the space along three axes: pedagogical role (tutor, learning coach/mentor, companion, instructor's assistant, curriculum planner), autonomy level (reactive → adaptive → proactive → collaborative), and embodiment (text-based, avatar/graphical, embodied/robotic). This taxonomy — particularly the autonomy spectrum and the checklist's exclusion of reactive tools — gives researchers and designers shared vocabulary for classifying agentic systems and distinguishing genuinely agentic from merely conversational AI.

A worked discriminator makes the line concrete. A static FAQ chatbot that can only answer a fixed set of questions meets zero criteria (no planning, no persistence, no initiative) and is plainly not agentic. A conversational tutor that remembers the current session but never acts unless prompted, holds no cross-session learner model, and cannot set sub-goals may meet only one or two (memory, some reasoning) — conversational, not agentic. By contrast, a tutor that plans multi-turn lessons, stores learner progress in a persistent profile, proactively fires a hint when a learner stalls, and re-plans the next step based on that profile satisfies planning, memory, autonomy, and goal-directed interaction — at least four criteria, so it qualifies as agentic. The value of running this test is not pedantry: labeling every LLM chat interface "agentic" blurs the very design question — what the system initiates versus what the learner must initiate — that determines whether it scaffolds or supplants learning.

Bounded agency as an educational design stance. Ilieva et al.'s (2026) AGAI-HE framework accepts the capability list that definitions such as the six-criteria checklist describe, then deliberately constrains it: goals, roles, data sources, tools, checkpoints, stopping conditions, and final decisions are defined or approved by educators, and each agentic function must trace to a learning requirement, assessment purpose, or governance control. The framework also draws a line between agentic orchestration and advanced prompting — an agentic workflow preserves task state, allocates functions, checks completion conditions, returns to earlier stages when evidence is insufficient, and records material decisions. Notably, its exploratory perception study with 130 higher-education students found no significant difference between agent-supported and chatbot-supported learning, which the authors read as evidence that agentic capability does not by itself produce a perceived pedagogical advantage (human oversight).

The field: rapid expansion and current shape

The knowledge base's scoping review — the most comprehensive synthesis of the field to date, mapping 474 studies (2020–2026) — documents a field that has grown explosively since 2025, but whose literature is still dominated by conference papers concentrated in Higher Education, STEM disciplines, and text-based tutoring scenarios. The review analyzes publication characteristics, study designs, agent roles, AI models and architectures, six dimensions of agentic capability, and the extent of educational-theory integration, providing a roadmap for the field's frontiers and gaps. Notably, only 29% of the reviewed studies (138 of 474) explicitly grounded their systems in educational theory, exposing a disciplinary divide between technically oriented and pedagogically oriented work.

A role-based map of the field

Where the 474-study scoping review and Kostopoulos et al.'s conceptual survey map research breadth and capability, Baradziej (2026) organizes agentic AI by the role it plays — an institutional framing for deciding where to deploy and govern these systems. Across 48 higher-education studies, six roles emerge in order of evidential weight: personalized learning and adaptive tutoring (18/48), Automated Assessment and feedback (12), teaching assistance and augmentation (11), administrative and student support (8), curriculum design and workforce alignment (5), and research support and academic operations (4). The role lens foregrounds a design choice that recurs in every deployment: how much moment-to-moment control the human retains (a "Copilot" relation) versus how much the agent owns (an "Autopilot" relation) — the autonomy axis that determines whether an agent scaffolds learning or supplants it.

Design and evaluation of agentic systems

Research in the knowledge base spans design and evaluation:

  • Hybrid agents grounded in theory outperform pure prompting: ISD-Agent-Bench, a benchmark of 25,795 instructional-design scenarios, finds the best-performing approach integrates classical ISD frameworks (ADDIE, Dick & Carey, Rapid Prototyping) with modern ReAct-style reasoning — hybrid (theory + technique) > pure theory > technique-only. Grounding Large Language Models (LLMs) agents in established educational-design theory provides a structural advantage raw prompting cannot replicate.
  • Assessment frameworks for agentic tools: The tool-invariant framework proposes Teaching and assessing computational methods in a way that does not depend on any specific AI tool, emphasizing Computational Thinking fundamentals, Authentic Assessment via oral defense, and verification — relevant to Over-Reliance concerns.
  • A reporting-and-governance scaler: the Autonomy–Oversight–Evidence (AOE) framework. Dey (2026) argues the term agentic AI is applied so inconsistently that evidence and oversight cannot be compared across studies — systems that plan, remember, use tools, or coordinate multiple agents are lumped with static GenAI interfaces and conventional pedagogical agents. His critical integrative review of fifteen peer-reviewed reviews finds evidence strongest for artifact-level outcomes (feedback accuracy, hallucination reduction) and weakest for durable learning, equity, workload, or institutional outcomes, with authentic deployments typically short, single-site, and weakly tied to oversight. The fix is a concrete reporting triplet — autonomy level A0–A4 (reactive generation → bounded orchestration → delegated task autonomy → workflow autonomy → consequential autonomy over grading/admissions/progression), oversight level O0–O4 (unspecified → retrospective audit → pre-use approval → checkpointed control → continuous bounded supervision), and evidence-maturity stage M0–M5 (concept → prototype/benchmark → participant evaluation → authentic deployment → extended/multi-site → replicated/institution scale) — plus a proportionality rule: allowable autonomy should not outrun either evidence maturity or oversight strength (e.g. an A4 consequential system demands M4–M5 evidence, legal validation, and O4 with human final authority). Reporting A2–O3–M3 turns vague "agentic deployment" claims into comparable, testable specifications and gives institutions a staged adoption, logging, and rollback grammar — the evidentiary complement to the Copilot-to-Autopilot spectrum above.
  • Adversarial robustness testing: Multi-agent stress testing coordinates Interrogator, Target, and Judge agents to reveal failure modes invisible to single-strategy testing, reducing robustness scores by 0.17–0.20 points — critical for persona consistency and safe deployment with learners.
  • Domain applications: agentic systems appear across domains, including adaptive learning agents, pedagogical LLM agents, guided LLM scaffolding, gamified cybersecurity learning agents, and clinical simulation agents.
  • Web agents as learning-experience evaluators: a single autonomous "describing" web agent that navigates an online lesson like a student — Wang, Mitchell & Piech (2025) — produces a description rich enough to predict student dropout and give the designer actionable feedback before real learners engage, outperforming a full simulated cohort and every baseline on a global CS1 course. This positions agentic evaluation (an agent as a stand-in critic of a learning experience) as a distinct, low-cost use of agentic AI alongside agents that teach or design.

Multi-agent systems

A growing and distinct strand of agentic AI involves multi-agent systems that orchestrate multiple specialized agents with distinct roles. The knowledge base documents several architectures: CODE-GEN: A Human-in-the-Loop RAG-Based Agentic AI System for Multiple-Choice Question Generation pairs a generator agent with a validator agent for human-in-the-loop question generation; adversarial testing coordinates Interrogator/Target/Judge agents; multi-agent classrooms (e.g., MAIC with teacher, TA, and classmate archetypes) create varied peer-learning dynamics; and multi-agent social learning explores how interacting agents shape learning. Multi-agent design raises distinctive questions about human oversight (which agent is accountable, and where does a human intervene?), coordination costs, and how role differentiation supports or complicates Scaffolding.

Two 2026 systems sharpen what role specialization buys — and where it stops paying. MeduAI-SP (Yang et al., 2026) splits clinical-interview instruction across four LLM agents — a patient agent, a Socratic tutor agent, a turn-level evaluator, and a final evaluator — each carrying a distinct instructional function. The architecture withholds answers by design: the tutor was barred from disclosing the diagnosis or case answers, and the final evaluator's OSCE scores stayed hidden during the live encounter. Role specialization, rather than raw model capability, is what the trial credits for improved consultation behavior (71.8% vs. 55.6% on the final examination), offering a concrete template for pedagogy-aligned multi-agent design. A second study points to the limits of that pattern: in a 45-student, 15-group ethics discussion system (Seo et al., 2026), three LLM personas embodying care, deontological and pragmatic ethics produced no significant differences among themselves, and all were rated below human peers on contribution, diversity and influence (all Kruskal-Wallis p < .001). The authors attribute the gap to sycophantic agreement — agents accepted nearly every contribution unless it was wholly wrong — and to a lack of visible reasoning, arguing that task-focused agent design must pair process transparency with safeguards (fallible-peer framing, a dedicated Questioning phase) to avoid over-reliance.

A step beyond orchestrating a few specialized agents is the full agentic multi-agent ecosystem proposed by Sudarshan et al. (2026): an institution-wide platform coordinating learning, teaching, and administrative agents through cross-functional feedback loops and distributed intelligence. Its distinctive move is treating inclusivity as a first-class architectural concern — coordinating Accessibility, cognitive-support, and Well-Being agents so that learners with special educational needs are supported across cognitive, sensory, and emotional dimensions in real time, rather than being served by an isolated assistive tool. The paper situates this inside a human–AI co-evolution loop (human behaviors and decisions shape AI adaptation, which in turn enhances human capability) that keeps humans in the loop.

  • Participant-specific LLM agents for collaborative problem solving. Fang (2026) fine-tunes individual LLM agents on real participants' dialogue data to represent each participant in collaborative problem solving simulations, with probabilistic speaker and thematic-code selection and sliding-window plus summarized memory. Validated with Epistemic Network Analysis, the simulated dialogues are statistically indistinguishable from real ones (ENA distance 0.17, permutation p = 0.65) — a demonstration of agentic AI reproducing authentic collaborative discourse.
  • Socially intelligent multi-agent tutoring. Socially intelligent multi-agent tutoring prototypes such as ASTRA study how learners coordinate with AI in dyads, using differentiated Tutor and Facilitator agents to prompt coordination and balanced participation. The framework's trace-based evaluation enables reproducible analysis of interaction, participation balance, and verification in introductory programming.

The central tension: automation vs. learning

The pedagogical best-practice work articulates the field's defining tension: as education AI shifts from passive chatbots to proactive agents that initiate and pursue goals, personalization improves but learner Learner Agency and cognitive effort are at risk. The more an agent automates, the less cognitive work the learner does. The design response — intentional friction, dynamic Scaffolding, Human-in-the-Loop oversight, and considered AI utilization — acts as a principled guardrail. This connects to Desirable Difficulties, Sociocultural Learning, and the risk of Over-Reliance, and to the broader theme of preserving Learner Agency in AI-mediated learning. One empirical check comes from a practical report on Spec-Driven Development in a software PBL course (Tanaka et al. 2026): students using AI agents across development phases generated more code but showed code-comprehension dips that recovered only after instructor one-on-one interviews - concrete field evidence for the automation-vs-learning tension and for Human-in-the-Loop monitoring as the mitigation.

The survey literature turns these principles into measurable design Guardrails rather than vague intentions. On scaffolding, Kostopoulos et al. (2025) recommend fading protocols — gradually reduce hint frequency after each successful attempt — and targeting a Help-Seeking ratio (AI-initiated hints ÷ total student actions) below 0.3, so the agent is not the one driving most of the interaction. They pair this with reflective checkpoints (e.g., ask the learner to explain their reasoning before the agent offers the next cue) and adaptive fading curves whose intervention likelihood drops as proficiency rises. On transparency, agents should expose a "Why this suggestion?" rationale and keep timestamped decision-traceability logs (agent rationale, data sources, decisions) available for instructional auditing. On fairness, they advise pre-deployment disparate-impact testing across at least three demographic groups (e.g., gender, language, geography) and involving diverse teachers in design. These metrics give an instructor or designer an audit lever: rather than asking "is the agent too helpful?", measure whether hints are fading, whether the learner is initiating, and whether the agent's reasoning is inspectable.

A complementary vocabulary for the autonomy question comes from the aviation analogy surfaced in Baradziej's (2026) synthesis: how far a deployment sits on the Copilot-to-Autopilot spectrum — from an agent that assists a human retaining moment-to-moment control, to one that owns the whole task with the human only supervising. The same underlying system can be configured toward either end, and the choice is pedagogical before it is technical. Copilot-style configurations (agent proposes, learner disposes, human retains final say) tend to preserve Learner Agency and support the effortful processes that build learning; Autopilot-style configurations maximize task completion and efficiency but shift the cognitive load off the learner. Selecting a position on this spectrum — per task, not once globally — is a concrete way to operationalize the field's "intentional friction" principle.

Positive implications of AI agents for education

When designed well, agentic AI offers substantial benefits:

  • Deeper, more adaptive personalization. Persistent agents can sustain a learning conversation over many turns, tracking what a learner knows, adapting difficulty, and sequencing multi-step Scaffolding — going beyond the one-shot responses of earlier chatbots. This supports adaptive and personalized learning at scale. In Baradziej's (2026) synthesis the strongest evidence for this role reports academic gains of 15–25% and engagement increases of up to +40%, with particular potential for learners historically ill-served by one-size-fits-all instruction (first-generation students, learning differences, second-language learners).
  • Unburdening routine instructional work. Agents can plan lessons, generate and validate questions, draft feedback, and orchestrate specialized sub-agents (e.g., generator + validator for question creation), freeing teachers for higher-value interaction. This is the promise of teacher-facing multi-agent workflows. Assessment agents in particular report 90–95% agreement with human graders and 50–70% reductions in grading time, though the same evidence flags bias and the metacognitive cost of instant, unreflective feedback.
  • Rich, varied interaction. Multi-agent classrooms and simulated peers create diverse interaction dynamics (peer-like discourse, constructive disagreement, role-play) that single-agent systems cannot, supporting Collaborative Learning, Socratic-style probing, and Simulation.
  • Productive friction. Agents designed to challenge rather than agree can push learners toward deeper reconsideration. Research on adversarial design agents shows that constructive-conflict agents prompted significantly more design iterations, broader exploration, and higher-rated final designs (N=48) — a form of desirable difficulty.
  • Scalable practice and simulation. Agent-based simulations (simulated students, clinical scenarios) let learners practice in low-risk environments before real-world application, as in clinical simulation and simulated learners.
  • Evidence-aware scaffolding. Well-grounded agents can apply learning theory and known pedagogy in their interactions, and benchmarks show theory-grounded agents outperform raw prompting.

A narrower, instructor-built class of agents gets a distinct argument in El Khoury and Ma's third-space proposal: custom GPTs, Gems and Copilot Studio agents designed by instructors for a specific pedagogical purpose and explicitly not autonomous systems. Their claimed value is as low-stakes rehearsal space — an oral-exam simulator, a clinical-communication simulation, an ESL pronunciation avatar — where students practice before judgment while the instructor's evaluative role stays intact, with the agent outside the social hierarchies students navigate with peers and instructors. Two boundaries are worth noting: the authors exclude agent-based grading from scope by design, and they argue assessment must remain relational — the instructor role cannot be substituted by a machine, though it can be extended and made more sustainable through careful design.

Negative implications and risks of AI agents for education

The same autonomy that enables these benefits also creates significant risks:

  • Erosion of learner agency and cognitive effort. The more an agent automates, the less cognitive work the learner does. Proactive agents that initiate, plan, and complete tasks can leave learners as passive consumers, hollowing out the effortful processes — drafting, recalling, revising — that build durable learning. This is the core Over-Reliance and Learner Agency concern. The effect is measurable: in Baradziej's (2026) synthesis, passive learners in agentic-tutoring environments underperformed Active Learning students by 8.7% — evidence that the harm follows deployment design (letting the agent do the cognitive work) more than the technology itself.
  • Over-automation of the learning process. If an agent optimizes for task completion rather than learning, it can produce "answers" that bypass understanding — the very risk the tool-invariant framework warns about, where the artifact no longer certifies the learner.
  • Reduced metacognitive and self-regulated engagement. When agents handle planning and monitoring, learners may not develop the Metacognition and self-regulation that education aims to build. Agents must be designed to 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. The need for robust verification and AI Literacy grows as agents take on more autonomy.
  • Opacity, coordination, and accountability. Multi-agent systems complicate human oversight: which agent is accountable for an error, and where does a human intervene? Coordination failures, persona drift, and emergent behaviors can undermine reliability and Pedagogical Safety.
  • Bias and equity. Agents trained on data that encode bias can reproduce it at scale, and unequal access to capable agentic systems can widen educational inequity. Bias operates at multiple levels — training data, architecture, evaluation criteria, and test populations — so it needs institutional mitigation (bias audits, diverse datasets, transparent documentation, stakeholder involvement), not one-time checks. A subtler, culturally specific form is epistemic hegemony: because most agentic systems are trained on English, Western-produced data, they embed particular assumptions about knowledge, argumentation, and academic register. Synthesis evidence documents language-education tools marginalizing non-Western rhetorical traditions and penalizing linguistic features of non-English academic cultures, and Global South analyses show agentic pedagogies reproducing inequity when they ignore epistemological diversity and infrastructure constraints.
  • Assessment integrity and skill decay. When agents can generate work on demand, assessing genuine learning becomes harder, and over-reliance can erode foundational skills — the "comprehension debt" and certification problem the field flags.
  • Ghost students and the verification gap. Bozkurt, Crompton & Fell Kurban (2026) describe the "ghost student" — a digital surrogate created by coupling LLMs (the "mind") with agentic AI browsers (the "body") that can navigate Learning Management Systems, engage with content, and complete assessments with human-like mimicry, making the actual learner's presence optional. This creates a verification gap that traditional proctoring and detection tools are structurally unable to close, and it accumulates cognitive debt in the learner who is bypassed. As AI shifts from generative to agentic, this integrity and verification threat grows — an agentic-specific risk beyond those of single-turn GenAI.

AI agents and academic integrity

Agentic AI poses distinctive integrity threats that go beyond the single-turn GenAI cases the field already struggles with. Because agents act autonomously over long horizons — and because "ghost students" (LLM "minds" coupled with agentic browser "bodies") can navigate Learning Management Systems, engage content, and complete assessments with human-like mimicry — they make the learner's genuine presence optional and create a verification gap that proctoring and detection cannot close. Several integrity implications follow:

  • The artifact no longer certifies the learner. When an agent can generate, plan, and execute an entire submission, the product's quality reflects the agent's capability, not the learner's. This is the tool-invariant certification problem at its extreme — traditional "submit the work" assessment loses its evidential value.
  • Verification, not detection, is the only viable response. Detection-based policing is structurally unable to keep up with autonomous agents. The integrity question shifts from "can we catch AI agents?" to "can we verify what the learner can actually do?" — favoring process-based, interactive, and Human-in-the-Loop verification.
  • Agentic completion of assessed coursework is now demonstrated, and it is a validity failure, not just an integrity one. Hadjisolomou & El-Haddad (2026) documented agents (Claude for Chrome, Perplexity Comet, Claude Opus) logging into a live undergraduate LMS course and completing real assessed work from a single instruction — a 10-question quiz scored 10/10 in under 5 minutes, and a discussion-board post in which the agent fabricated a credible personal life story after mining peers' posts. Applying Kane's argument-based validity framework, they place a "human-production assumption" at the base of the scoring inference: agent completion removes its backing, so every unproctored asynchronous score — including honestly earned ones — loses interpretive support because authorship is unverifiable. Framing the problem as validity rather than integrity matters because an institution can punish misconduct and still lack grounds for the scores it reports, and because the same artifacts feed program-review and accreditation evidence chains. Their remedy, aligned with this page's "verification over detection," is assessment redesign for verified human presence (presence over product, integration over isolation, authenticity over genericity, low-stakes practice / high-stakes presence) with an equity-preserving menu of verified-moment options.
  • Accountability is diffused. In multi-agent systems, when an autonomous agent produces problematic output, it is unclear who is accountable — the learner, the system, or the institution. This blurs the attribution that academic-integrity processes assume.
  • Cognitive debt accumulates silently. Ghost students let learners bypass the effortful processes that build understanding, accruing cognitive debt that surfaces only when independent performance is required. Integrity is thus tied to genuine learning, not just rule-compliance.
  • It widens equity gaps. Learners with access to more capable agentic systems gain an outsized advantage, and automated support may erode help for those who need it most — an Equity dimension of integrity.

This connects the agentic-AI discussion to the knowledge base's Academic Integrity coverage, which frames the response as assessment redesign and AI Literacy rather than detection alone.

Productive friction and social interaction

Not all agentic behavior need be smooth assistance. Research on adversarial design agents shows that agents enacting constructive conflict prompted significantly more design iterations, broader exploration of alternatives, and higher-rated final designs among novice interaction designers (N=48) — a productive friction dynamic, where the conflict agent was frustrating but ultimately helpful. This connects to Socratic questioning and Design Thinking, and illustrates how agentic AI can support deep reconsideration rather than passive acceptance.

A study of how agentic AI reaches learning outcomes through psychological rather than technological pathways supplies the missing measurement angle. Pramod and Patil (2026) surveyed 398 business students in India and modeled autonomy, competence and relatedness alongside interactivity, information sharing and perceived social presence; autonomy was the strongest motivational driver (β = 0.504) and interactivity the strongest social one (0.468), with motivation and social presence feeding engagement at nearly the same strength (0.533 and 0.493) before engagement predicted perceived learning performance (0.671). The design lesson is that the social route is not automatic: a collaborative-environment construct moved perceived social presence less than plain responsiveness and information sharing did, so treating an agent as a chat interface rather than a participant leaves most of that pathway unused.

Implications for instructors and instructional designers

For teachers, faculty, and instructional designers, agentic AI changes both what is possible and what must be guarded:

  • Reallocate effort to higher-value work. Agents can take over lesson planning, question generation and validation, feedback triage, and resource retrieval. Instructors should treat these as automatable scaffolds that free time for what agents cannot do: relational teaching, contextual judgment, and the design of learning experiences. Teacher-facing multi-agent workflows are a promising model.
  • Keep the learner's cognitive work front and center. The central design question is not "what can the agent do?" but "what must the learner do?" Instructional designers should configure agentic systems so they scaffold rather than replace learner planning, monitoring, and effort — using dynamic Scaffolding and intentional friction to protect Learner Agency and avoid over-reliance.
  • Design for verification and process, not just output. When agents can generate work on demand, the artifact no longer certifies learning. Instructors should pair agentic tools with process-based assessment (oral defense, tool-invariant tasks, verification checks) so that understanding — not just production — is measured.
  • Curate and ground agents in pedagogy. Benchmark evidence shows theory-grounded agents outperform raw prompting. Designers should ground agent behavior in established instructional frameworks (e.g., gradual release, Socratic questioning, learning theory) rather than defaulting to generic tool-chaining.
  • Retain human oversight and judgment. Multi-agent and autonomous systems make Human-in-the-Loop design essential: decide where a human intervenes, who is accountable, and how failures are caught. Adversarial testing helps surface failure modes before deployment.
  • Build instructor AI Literacy. Teachers and designers need accurate mental models of agentic AI to configure, monitor, and critique these systems — and to model responsible use for learners. This links to Teacher AI Competency and faculty development.
  • Watch for equity. Agentic tools risk widening gaps if access is unequal or if automation erodes support for the learners who need it most; design with Equity in mind.
  • Stand up institutional scaffolding before scaling. The deployment question is not only design-level but institution-level. Baradziej's (2026) synthesis condenses the governance evidence into three pillars: develop AI Literacy among students and staff; build ethical infrastructure (data-protection policies, algorithmic-accountability and academic-integrity frameworks) before large-scale deployment; and deliver competence-based educator training that goes beyond tool familiarization to pedagogical frameworks preserving human agency. Given only ~6.5% of faculty in some national contexts report direct AI use, the training gap is a binding constraint on responsible adoption.

Techniques for ensuring academic integrity with agentic AI

Because autonomous agents make detection futile, instructors should focus on techniques that verify learning and make honest work visible, rather than on policing:

  • Prefer verification over detection. Replace or supplement "submit the work" with interactions that require the learner to demonstrate understanding they cannot outsource: oral defenses, tool-invariant tasks, live Problem Solving, and process-based assessment. The goal is to establish what the learner can do independently, not to catch an agent.
  • Use interactive and staged assessment. Require staged submissions (drafts, revisions, reflections) and follow-up conversational checks that probe whether students understand their submitted work — the "AI Viva" and cognitive-stewardship approaches. Ghost students cannot sustain a live interrogation they did not perform.
  • Set clear, purpose-driven expectations. Ground integrity expectations in the course's purpose — what AI use is allowed, when, and why — rather than abstract rules. Policy clarity that is aligned with pedagogy reduces the ambiguity students exploit and the misjudgments documented in integrity research.
  • Make AI use visible and declared. Structured, task-specific AI-use declarations (mapping use to cognitive stages) force reflection and normalize honest disclosure, shifting the culture from concealment to transparency.
  • Build AI literacy as integrity education. Teach students how to use agents responsibly and to judge output critically, framing integrity as genuine learning rather than rule-following. This includes AI Literacy, understanding what agents can and cannot do, and the learning cost of bypassing effort.
  • Keep humans in the loop. Maintain human oversight of assessment decisions, verify high-stakes submissions interactively, and design agentic tools so an instructor can always intervene.
  • Close the verification gap with interaction. For fully online or asynchronous contexts, use proctored or interactive components that require live presence, addressing the ghost-student threat directly rather than assuming detection will catch it.

A balanced takeaway

Agentic AI is neither a panacea nor an inevitable harm: its value depends on design. Used to scaffold learner agency, ground in pedagogy, and keep humans in the loop, agents can personalize and deepen learning; used to maximize automation and task completion, they can erode the very effort that produces learning. The recurring design principle is intentionality — deciding explicitly what the agent does and what it deliberately leaves for the learner.

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Metadata

DisciplineStem Education
LevelHigher Ed, K 12
Intended audienceLearners
Learning and instructionScaffolding
Technologies and techniquesGenerative AI, Human-in-the-Loop, Intelligent Tutoring, Large Language Models (LLMs)
Foundations of AI in educationLearner Agency, Agentic AI, AI Literacy, Cognitive Offloading
SourceHistoryEditChat

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