🏷️ human-in-the-loop
24 pages tagged with human-in-the-loop(21 articles, 3 concepts)
📄 HAIML: A Human-Centered AI Metacognitive Learning Model — A Framework for Human Agency and Reflective Learning in the Age of Artificial Intelligence
> **Synthesis:** HAIML is a human-centered framework for learning in AI-supported environments that preserves human agency, metacognitive awareness, ethical reasoning, and personal responsibility. Gro…
2026-08-12 · metacognition, self-regulated-learning, ai-literacy, cognitive-offloading, over-reliance
📄 VeriForge: Mitigating Latent Knowledge Gaps in Narrative Drafting via Mixed-Initiative Scaffolding
> **Synthesis:** Sun et al. (2026) present VeriForge, a mixed-initiative [[generative-ai]] writing system that assumes initiative over domain discovery while the author retains initiative over narrati…
📄 Vibe Compiler: A Research-Logic Synthesis Tool That Runs without Prompt Engineering -Toward Enhancing Metacognition for Sustaining Agency in the Age of Generative AI-
> **Synthesis:** This paper introduces the Synthesis-Analysis Reciprocity Model and the Vibe Compiler tool to preserve human epistemic agency during GenAI-assisted intellectual work. The model frames …
📄 AI Literacy for Legal Translation: Developing Digital Resilience
> **Synthesis:** Proposes a four-component AI literacy framework for legal translation professionals: conceptual AI knowledge, technical operational skills, critical evaluation competencies, and ethic…
📄 Calibrating Trustworthiness: Co-Designing Metrics and Visualizations for Evaluating LLMs in Education
> **Calibrating Trustworthiness: Co-Designing Metrics and Visualizations for Evaluating LLMs in Education** — Longitudinal co-design with learning engineers building an LLM-powered digital textbook. C…
📄 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…
📄 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 …
📄 CoTAL: Human-in-the-Loop Prompt Engineering for Generalizable Formative Assessment Scoring and Feedback
> 1. **Evidence-Centered Design (ECD)** — assessments and rubrics aligned to curriculum goals from the start 2. **Human-in-the-loop prompt engineering** — labelled examples and prompts refined iterati…
2026-08-03 · formative-assessment, automated-grading, prompt-engineering, benchmark, ai-ed-evaluation
📄 Unanticipated Effects of Generative AI on Expertise Pathways and Performance Perception in System Administration
> **Rana Abou Khamis, Hala Assal, Ashraf Matrawy** — arXiv preprint (2026).…
2026-08-03 · generative-ai, professional-training, cognitive-offloading, over-reliance, lifelong-learning
📄 To Facilitate or not to Facilitate: Human and LLM Facilitator Tendencies in Online Discussions
> **Dimitris Tsirmpas, Katerina Korre, John Pavlopoulos** — arXiv preprint (2026).…
📄 Responsible Assessment in the AI Era: Key Insights from a Future-Focused Conference
> **Responsible assessment in the AI era** — assessment grounded in learners' sociocultural contexts and designed to generate valid, trustworthy, context-specific inferences from accumulated evidence,…
📄 Ordered Network Analysis of Epistemic Emotions during Collaborative Problem Solving
Anindho, Venkatesha, Ocumpaugh and Blanchard apply Ordered Network Analysis to trace how epistemic emotions such as confusion and frustration persist and transition during co-situated collaborative pr…
📄 Exploring How Agent Voice Accents Shape Human-AI Collaboration in K-12 Group Learning
**Ravi, Stevens, Hurt, Hanks, Lin & Anderson (2026)**. Ravi et al. investigate how the voice accent of a [[generative-ai]] conversational peer agent shapes learners' perceptions, trust, and interactio…
📄 Human-AI collaboration in higher education: Exploring the impact of technology expectations and distrust
> **Synthesis:** Human-AI collaboration in higher education: Exploring the impact of technology expectations and distrust…
📄 Benchmarking Large Language Models for Diagnosing Students' Cognitive Skills from Handwritten Math Work
> **MathCog** benchmark (3,036 teacher-annotated diagnostic verdicts, 639 handwritten responses, 18 LLMs): all models severely underperform (macro F1 < 0.5) — over-attributing evidence, overthinking m…
🏷️ 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…
🏷️ DOT Framework Survey: Practitioner Beliefs and Behaviors in AI-Enhanced Education
A 2026 cross-sectional survey (n=72) by Gibson, Azukas, and Knezek examined how higher education practitioners think about and use AI in teaching, grounded in the **DOT Framework** — a synthesis of [[…
📄 MindCopilot: Towards Formalizing and Evaluating Granular Human-LLM Co-Writing
MindCopilot introduces a formal framework for evaluating human-LLM co-writing that shifts from output-only metrics (BLEU, ROUGE) to **interaction-aware evaluation**. The paper models co-writing as a *…
📄 Design Principles and Observable Indicators for AI-Enabled Pedagogical Accompaniment: Evidence from the Amico Dual-Mode Prototype in Italy and China
Benedetti (2026) introduces a theoretically grounded framework for AI-enabled pedagogical accompaniment that explicitly centers human agency — an approach described as "human-in-command" rather than m…
📄 Balancing Teacher and Student Agency: Co-Orchestration Tool Design Supporting Real-Time Dynamic Pairing
Yang et al. (2026) tackle a fundamental tension in AI-augmented classrooms: how to balance teacher orchestration with student agency during dynamic transitions between individual and collaborative wor…
📄 Modernizing Ground Truth: Four Shifts Toward Improving Reliability and Validity in AI in Education
> Stop treating κ > 0.8 as a binary stamp of approval.…
📄 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.…
2026-05-08 · agentic-ai, ai-literacy, k-12, pedagogical-safety, agentic-ai-ecosystems-higher-education
📄 Text Simplification for Intelligent Tutoring
> **MuTSE** (Roscan et al., 2026) addresses a critical need in **Intelligent Tutoring Systems (ITS)**: delivering content at the right reading level for each learner. > Human-in-the-loop evaluation fr…
2026-05-08 · intelligent-tutoring, nlp-education, adaptive-learning, generative-ai, human-in-the-loop-ai
🏷️ Human-in-the-Loop AI for Education
Educational AI systems that strategically interleave automated generation with human expert judgment, preserving pedagogical quality while scaling production. Two recent implementations illustrate dis…