The Empty Quadrant: AI Teammates for Embodied Field Learning

• URL: http://arxiv.org/abs/2603.04034v1
• Date: Wed, 04 Mar 2026 13:13:44 GMT
• Title: The Empty Quadrant: AI Teammates for Embodied Field Learning
• Abstract summary: Field Atlas is a framework that shifts AIED's guiding metaphor from instruction to sensemak-ing.<n>We demonstrate the framework through a museum scenario and argue that the resulting trajecto-ries constitute process-based evi-dence structurally resistant to AI fabrication.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: For four decades, AIED research has rested on what we term the Sedentary Assumption: the unexamined design commitment to a stationary learner seated before a screen. Mobile learning and museum guides have moved learners into physical space, and context-aware systems have delivered location-triggered content -- yet these efforts predominantly cast AI in the role of information-de-livery tool rather than epistemic partner. We map this gap through a 2 x 2 matrix (AI Role x Learning Environment) and identify an undertheorized intersection: the configuration in which AI serves as an epistemic teammate during unstruc-tured, place-bound field inquiry and learning is assessed through trajectory rather than product. To fill it, we propose Field Atlas, a framework grounded in embod-ied, embedded, enactive, and extended (4E) cognition, active inference, and dual coding theory that shifts AIED's guiding metaphor from instruction to sensemak-ing. The architecture pairs volitional photography with immediate voice reflec-tion, constrains AI to Socratic provocation rather than answer delivery, and ap-plies Epistemic Trajectory Modeling (ETM) to represent field learning as a con-tinuous trajectory through conjoined physical-epistemic space. We demonstrate the framework through a museum scenario and argue that the resulting trajecto-ries -- bound to a specific body, place, and time -- constitute process-based evi-dence structurally resistant to AI fabrication, offering a new assessment paradigm and reorienting AIED toward embodied, dialogic human-AI sensemaking in the wild.

Related papers

• ArtAnno: Annotating Implicit Semantics in Artworks through LLM Agent-Driven Bidirectional Human-AI Augmentation [11.3983038688347]
  Current AI-assisted annotation tools often lack assistance or rely on one-way calibrations to improve AI models.<n>We propose Bi Human-AI Augmentation, a closed-loop framework in which skills and domain knowledge base evolve through real-time interaction and bidirectional HAI augmentation.<n>We implement this framework in ArtAnno, an artwork annotation system driven by a multi-agent architecture.
  arXiv  Detail & Related papers  (2026-08-05T16:30:12Z)
• Beyond AI Delegation: A Prompt Pattern Framework for Productive Struggle and Evaluative Judgement in Secure Coding Education [0.45706494756895794]
  Large language models make it easy for students to delegate writing, analysis, and problem-solving to automated systems.<n>We introduce a practical framework that helps educators keep GenAI in the course without removing the cognitive demands that make it worthwhile.
  arXiv  Detail & Related papers  (2026-05-23T07:35:49Z)
• Defining AI Fatigue in Academic Contexts: Dimensions, Indicators, and a Stage-Based Model Using Grounded Theory [0.0]
  The integration of AI tools in academic settings has introduced a distinct form of strain that existing frameworks like technostress and digital fatigue have not yet fully addressed.<n>This study develops a conceptual model and identifies the dimensions that define AI fatigue as a form of strain arising from sustained academic use of AI tools.
  arXiv  Detail & Related papers  (2026-05-22T00:46:39Z)
• Why AI systems don't learn and what to do about it: Lessons on autonomous learning from cognitive science [79.50667364149979]
  We critically examine the limitations of current AI models in achieving autonomous learning and propose a learning architecture inspired by human and animal cognition.<n>The proposed framework integrates learning from observation (System A) and learning from active behavior (System B) while switching flexibly between these learning modes as a function of internally generated meta-control signals (System M).
  arXiv  Detail & Related papers  (2026-03-16T14:54:56Z)
• AI Phenomenology for Understanding Human-AI Experiences Across Eras [8.54750896635797]
  We argue for AI phenomenology: a research stance that asks "How did it feel?" when interacting with AI systems.<n>We motivate AI phenomenology as a framework that captures how alignment is experienced, negotiated, and updated between users and AI systems.
  arXiv  Detail & Related papers  (2026-03-09T23:26:46Z)
• Towards Agentic Intelligence for Materials Science [73.4576385477731]
  This survey advances a unique pipeline-centric view that spans from corpus curation and pretraining to goal-conditioned agents interfacing with simulation and experimental platforms.<n>To bridge communities and establish a shared frame of reference, we first present an integrated lens that aligns terminology, evaluation, and workflow stages across AI and materials science.
  arXiv  Detail & Related papers  (2026-01-29T23:48:43Z)
• Emergent, not Immanent: A Baradian Reading of Explainable AI [37.51348424835944]
  We argue that interpretations emerge from situated entanglements of the AI model with humans, context, and the interpretative apparatus.<n>We propose design directions for XAI interfaces that support emergent interpretation.
  arXiv  Detail & Related papers  (2026-01-21T14:32:40Z)
• Toward Ultra-Long-Horizon Agentic Science: Cognitive Accumulation for Machine Learning Engineering [59.18634614089481]
  We present ML-Master 2.0, an autonomous agent that masters ultra-long-horizon machine learning engineering (MLE)<n>By reframing context management as a process of cognitive accumulation, our approach introduces Hierarchical Cognitive Caching (HCC)<n>HCC allows agents to decouple immediate execution from long-term experimental strategy.<n>In evaluations on OpenAI's MLE-Bench under 24-hour budgets, ML-Master 2.0 achieves a state-of-the-art medal rate of 56.44%.
  arXiv  Detail & Related papers  (2026-01-15T13:52:04Z)
• Embodied AI: From LLMs to World Models [65.68972714346909]
  Embodied Artificial Intelligence (AI) is an intelligent system paradigm for achieving Artificial General Intelligence (AGI)<n>Recent breakthroughs in Large Language Models (LLMs) and World Models (WMs) have drawn significant attention for embodied AI.
  arXiv  Detail & Related papers  (2025-09-24T11:37:48Z)
• Strategic Integration of AI Chatbots in Physics Teacher Preparation: A TPACK-SWOT Analysis of Pedagogical, Epistemic, and Cybersecurity Dimensions [0.0]
  The study was conducted within a university-level capstone course on innovative tools for physics instruction.<n>Results highlight internal strengths such as enhanced information-seeking behavior, scaffolded pedagogical planning, and support for symbolic reasoning.<n> External opportunities were found in promoting inclusive education, multilingual engagement, and expanded zones of proximal development.
  arXiv  Detail & Related papers  (2025-07-20T08:04:07Z)
• Embodied Exploration of Latent Spaces and Explainable AI [0.0]
  In this paper, we explore how performers' embodied interactions with a Neural Audio Synthesis model allow the exploration of the latent space of such a model. We provide background and context for the performance, highlighting the potential of embodied practices to contribute to developing explainable AI systems.
  arXiv  Detail & Related papers  (2024-10-18T16:40:34Z)
• Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AI [116.8199519880327]
  Embodied Artificial Intelligence (Embodied AI) is crucial for achieving Artificial General Intelligence (AGI)<n>In this survey, we give a comprehensive exploration of the latest advancements in Embodied AI.
  arXiv  Detail & Related papers  (2024-07-09T14:14:47Z)
• Two in One Go: Single-stage Emotion Recognition with Decoupled Subject-context Transformer [78.35816158511523]
  We present a single-stage emotion recognition approach, employing a Decoupled Subject-Context Transformer (DSCT) for simultaneous subject localization and emotion classification. We evaluate our single-stage framework on two widely used context-aware emotion recognition datasets, CAER-S and EMOTIC.
  arXiv  Detail & Related papers  (2024-04-26T07:30:32Z)
• A call for embodied AI [1.7544885995294304]
  We propose Embodied AI as the next fundamental step in the pursuit of Artificial General Intelligence. By broadening the scope of Embodied AI, we introduce a theoretical framework based on cognitive architectures. This framework is aligned with Friston's active inference principle, offering a comprehensive approach to EAI development.
  arXiv  Detail & Related papers  (2024-02-06T09:11:20Z)
• BEyond observation: an approach for ObjectNav [0.0]
  We present our exploratory research of how sensor data fusion and state-of-the-art machine learning algorithms can perform the Embodied Artificial Intelligence (E-AI) task called Visual Semantic Navigation. This task consists of autonomous navigation using egocentric visual observations to reach an object belonging to the target semantic class without prior knowledge of the environment. Our method reached fourth place on the Habitat Challenge 2021 ObjectNav on the Minival phase and the Test-Standard Phase.
  arXiv  Detail & Related papers  (2021-06-21T19:27:16Z)
• Language-guided Navigation via Cross-Modal Grounding and Alternate Adversarial Learning [66.9937776799536]
  The emerging vision-and-language navigation (VLN) problem aims at learning to navigate an agent to the target location in unseen photo-realistic environments. The main challenges of VLN arise mainly from two aspects: first, the agent needs to attend to the meaningful paragraphs of the language instruction corresponding to the dynamically-varying visual environments. We propose a cross-modal grounding module to equip the agent with a better ability to track the correspondence between the textual and visual modalities.
  arXiv  Detail & Related papers  (2020-11-22T09:13:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.