🏷️ agentic-ai
39 pages tagged with agentic-ai(35 articles, 4 concepts)
📄 CyberAGENTS: Structured Autonomy for Agentic Gamified Learning in Cybersecurity
> **Synthesis:** Hornung et al. (2026) present **CyberAGENTS**, an agentic framework for gamified cybersecurity learning that enables *structured autonomy* through ontology-guided validation, schema-g…
📄 Agentic AI-driven Immersive Simulation: A Knowledge-Aware Virtual Training Platform for High Dose Rate (HDR) Brachytherapy
> **Synthesis:** Xu et al. (2026) present an agentic AI-driven immersive simulation for training in **High Dose Rate (HDR) brachytherapy**, integrating VR and mobile computing to create a high-fidelit…
📄 Findings of the First Teaching Monster Challenge: A Benchmark of Pedagogical Content Knowledge in AI Agents
> **Synthesis:** Lin et al. (2026) present the **Teaching Monster Challenge**, the first instructional-video generation benchmark that treats the learner persona as an explicit evaluation criterion, m…
📄 AgentSchool: An LLM-Powered Multi-Agent Simulation for Education
> Ye et al. (2026) introduce **AgentSchool**, an LLM-driven multi-agent [[simulating-students|simulator]] that models learning as **state transition rather than prompted behavior**. It couples cogniti…
📄 Embracing Imperfection: Simulating Students with Diverse Cognitive Levels Using LLM-based Agents
> Wu et al. (2025, ACL) tackle the core challenge of [[simulating-students]]: LLMs trained as "helpful assistants" produce overly perfect answers and fail to model the natural imperfections and varied…
📄 Simulating Students with Large Language Models: A Review of Architecture, Mechanisms, and Role Modelling in Education with Generative AI
> Marquez-Carpintero, Lopez-Sellers & Cazorla (2025) present a thematic review of empirical and methodological studies using LLMs to [[simulating-students|simulate student behavior]] in education. The…
🏷️ Simulating Students
> **Simulating students** — using LLM-based agents to model learner behavior, cognition, and social dynamics for educational research, design, and training. Simulated students let researchers evaluate…
2026-08-12 · simulation, student-modeling, knowledge-tracing, cognitive-diagnosis, pedagogical-agent
📄 From Prompts to Verified Loops: The PCHL-HE Framework for Generative AI-Assisted Educational and Research Content Creation in Higher Education
> **Synthesis:** This conceptual preprint develops the Prompt-Context-Harness-Loop Framework for Higher Education (PCHL-HE), a pedagogically grounded vocabulary that differentiates four increasingly c…
📄 Adversarial Stress Testing of Role-Playing Language Agents using Multi-Agent Evaluation
> **Synthesis:** This paper presents a modular multi-agent platform for adversarially stress-testing [[agentic-ai|role-playing language agents]] through structured multi-turn dialogue. With three coor…
📄 Enacting Constructive Conflicts with AI Agents to Enhance Reconsideration among Novice Interaction Designers
> **Synthesis:** Investigates adversarial AI design agents that enact constructive conflict to prompt reconsideration in novice designers. Between-subjects experiment (N=48) comparing adversarial vs. …
2026-08-06 · design-thinking, higher-ed, scaffolding, student-ai-interaction, collaborative-learning
📄 When Agents Learn to Be You: Benchmarking Privacy Leakage, Impersonation Risk, and Defenses in Persona Skills
> **When Agents Learn to Be You: Benchmarking Privacy Leakage, Impersonation Risk, and Defenses in Persona Skills** — Introduces AntiSkillBench with 7,500 persona-grounded dialogue traces from 50 beha…
📄 EduClaw-Bench: A Long-Horizon Benchmark for Pedagogical LLM Agents with Simulated Learners
> **EduClaw-Bench: A Long-Horizon Benchmark for Pedagogical LLM Agents with Simulated Learners** — Introduces a 30-day long-horizon benchmark for pedagogical LLM agents using simulated learners ground…
📄 CODE-GEN: A Human-in-the-Loop RAG-Based Agentic AI System for Multiple-Choice Question Generation
> **A dual-agent RAG-based system for generating and validating coding comprehension MCQs**, evaluated by 6 SMEs across 7 pedagogical dimensions (N=288 questions, 2,016 rating pairs). AI excels at cri…
📄 DeepTutor: Towards Agentic Personalized Tutoring
> **A fully open-source agentic tutoring framework that closes the loop between citation-grounded problem tutoring and difficulty-calibrated question generation**, powered by a hybrid personalization …
📄 From MOOC to MAIC: Reshaping Online Teaching and Learning through LLM-driven Agents
> **A new paradigm for online education replacing MOOCs with LLM-driven multi-agent AI classrooms**, piloted at Tsinghua University with 100K+ learning records from 500+ students. MAIC uses specialize…
📄 The agency gap in AI-supported writing: how reactive and proactive agent designs shape multimodal reasoning
A randomized experiment (n = 79 medical/nursing students) examining how the **initiative design** of an AI writing agent shapes reasoning, agency, and immediate independent performance. Students compl…
📄 Agentic AI and Pedagogical Best Practice: The Tension Between Automation and Learning
> Education AI is shifting from passive chatbots to **proactive agents** that initiate and pursue goals. This offers personalisation but risks undermining **learner agency and cognitive effort**. The …
2026-08-03 · educational-theory, scaffolding, formative-assessment, metacognition, human-in-the-loop
📄 Trust and Reliance on AI in Education: AI Literacy and Need for Cognition as Moderators
> Pitts, Rani & Mildort (2026, AIED) show with 432 undergraduates that **higher trust in an AI assistant is associated with lower appropriate reliance**: students who trusted the assistant more were w…
🏷️ Agentic AI in Education
> **Agentic AI** — 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:…
📄 Agentic AI in Education: A Scoping Review of Research Landscape, Capabilities, and the Frontier Agent Paradigm
Published in *Computers and Education: Artificial Intelligence*, accepted 27 July 2026. 📄 doi:10.1016/j.caeai.2026.100653…
📄 ISD Agent Benchmark
> **ISD-Agent-Bench** is a comprehensive benchmark for evaluating LLM-based instructional design agents, comprising **25,795 scenarios** generated via a Context Matrix framework that combines 51 conte…
📄 A Tool-Invariant Framework for Teaching and Assessing Computational Methods in the Age of Agentic AI
> **Larry Engelhardt (Francis Marion University)** — *arXiv:2607.15518* [physics.ed-ph], submitted 17 Jul 2026. CC BY 4.0. doi:10.48550/arXiv.2607.15518. > **Note on type:** This is a *framework / pos…
📄 Agents That Teach: Designing Incidental Learning Back into AI-Assisted Software Development
As AI coding agents take over substantial implementation work, developers increasingly lose the informal, effortful problem-solving through which software engineering expertise historically accumulate…
📄 Prompt Coach: An Empirical Evaluation of an Agentic Tutor for Learning Prompt Engineering in Software Development
Prompt engineering is a critical yet undertaught skill for software developers, poorly served by traditional instruction because of its evolving, interactive, context-dependent nature. The authors int…
📄 From Memorization to Creation: Evaluating the Cognitive Depth of LLM-Generated Educational Questions
LLM-generated educational questions show varying cognitive depth; models excel at factual recall but struggle with higher-order thinking questions per Bloom's taxonomy. From Memorization to Creation: …
📄 Measuring Whether LLM Tutors Teach or Solve: A Diagnostic for Educational Impact
Studies whether public LLM tutoring benchmarks distinguish learning-supportive behavior from mere answer production. Proposes a lightweight diagnostic based on the gap between solving-oriented and ped…
📄 ASE-26: A Curriculum for Agentic Software Engineering as a Discipline
Formalizes Agentic Software Engineering (ASE) as a distinct discipline. Proposes a 21-module curriculum focused on the "evolution of intent" and practitioner discipline required to manage agents rathe…
🏷️ AI Ed Evaluation
> **AI-ed evaluation** — the body of methods, benchmarks, and criteria used to assess whether AI education tools (LLM-based tutors, automated graders, feedback systems, agents) actually work — not jus…
📄 Agentic Literacy Debt: A Structural Problem the AI Literacy Field Has Not Yet Named
**Agentic Literacy Debt** names a critical gap in the [[ai-literacy]] landscape that has become urgent with the rise of autonomous AI agents. Existing AI literacy frameworks assume humans evaluate AI …
📄 Persistent AI Agents in Academic Research: A Single-Investigator Implementation Case Study
This is the first empirical study of what happens when AI agents are embedded **persistently** in a real academic research environment — with durable memory, local files, external tools, scheduled rou…
📄 AI-Enabled Serious Games: Integrating Intelligence and Adaptivity in Training Systems
Serious games are widely used for learning and training across domains such as healthcare, defense, and education. This chapter examines how contemporary AI approaches may support real-time instructio…
📄 Are Agents Ready to Teach? A Multi-Stage Benchmark for Real-World Teaching Workflows
> Are Agents Ready to Teach? A Multi-Stage Benchmark for Real-World Teaching Workflows **Chen et al. (2026)** — Multiple institutions. Under review.…
📄 LearnMate^2: Design and Evaluation of an LLM-powered Personalized and Adaptive Support System for Online Learning
> LearnMate^2: Design and Evaluation of an LLM-powered Personalized and Adaptive Support System for Online Learning **Wang, Lee, & Mutlu (2026)** — University of Wisconsin-Madison. CHI-related publica…
📄 When AI Agents Teach Each Other: Discourse Patterns Resembling Peer Learning in the Moltbook Community
> **Authors:** Eason Chen, Ce Guan, A Elshafiey, Zhonghao Zhao, Joshua Zekeri, Afeez Edeifo Shaibu, Emmanuel Osadebe Prince **Year:** 2026 **Venue:** arXiv (cs.HC) > Mining discourse from Moltbook, a …
📄 Beyond the AI Tutor: Social Learning with LLM Agents
Most AI-based educational tools adopt a one-on-one tutoring paradigm, pairing a single LLM with a single learner. Yet decades of learning science — from Vygotsky's Zone of Proximal Development to Band…
📄 Agentic Education with AI Coding Assistants
> AI coding assistants proliferate rapidly, but pedagogical frameworks for learning them remain scarce — a paradox at the heart of agentic coding education. > Using agentic AI workflows (Claude Code) …
📄 Multi-Agent Systems for Instructional Design
> Embedding the Knowledge–Learning–Instruction (KLI) framework into multi-agent systems to act as sophisticated instructional designers for K-12 educators.…
📄 Agentic Workflows in Education
> A design framework for educational AI systems structured around four agentic paradigms: **reflection**, **planning**, **tool use**, and **multi-agent collaboration**. Proposed by Kamalov et al. (202…
🏷️ Formative Assessment in AI Education
Assessment designed to inform ongoing instruction and learning, as opposed to summative evaluation. AI systems can generate, validate, and adapt formative assessment items at scale, though quality var…