A conceptual framework for reflective AI is developed by reinterpreting four existing functions by reinterpreting four existing functions (conversational feedback, pronunciation assessment, adaptive personalization, and gamification) through Schön’s reflection-in-action and models of metacognitive regulation.
Emre Dinç, Fatih Toy· International journal of tec...· 0 citations
Principal leadership plays a strategic role in creating a work environment that supports improvements in teaching quality and teacher instructional performance. In the context of changing demands in education, principals are expected not only to manage schools but also to implement participative and adaptive leadership...
Different contributions of opioid and dopaminergic signaling to the production, reinforcement, and fine‐tuning of species‐specific songs are discussed, and ways in which reward associated with song production outside the breeding context contributes to flock cohesion are highlighted.
The comparative performance of the proposed EdgeTwin-DRL architecture within the simulated environment is demonstrated and motivated to motivate further investigation of edge-assisted digital twins for counter-drone decision support.
Unknown authors· Italian National Conference...· 0 citations
The application of JITAIs in mental health is developing rapidly and has become a hot topic in interdisciplinary research, however, research capacity is unevenly distributed, and international collaboration needs strengthening.
Lu Luo, Ai-Min Wang, Jian-Hui Zhou et al.· Medicine· 0 citations
T-CARE is introduced, a hybrid learning-heuristic multi-UAV coordination framework that integrates zero-shot constrained action selection with priority-aware temporal reservations and achieves 100% success, 0% collision rate, and no observed persistent starvation or deadlock.
Abhudaya Shrivastava, Z. Obradovic· Integrated Computer-Aided En...· 0 citations
The method, AlignGraft, aligns a larger, frozen, never-tuned model by adding the pair's velocity difference during sampling by adding the pair's velocity difference during sampling, and preserves the large model's fidelity at a small constant sampling overhead.
This work identifies a structural failure mode of this method class on terminal-navigation tasks: reaching a precise goal configuration while satisfying safety constraints that tighten along the final approach, and empirically demonstrates that adding a dense per-step success signal via an auxiliary value critic improv...
Aman Arora, Ricard M. Castan, Matteo El-Hariry et al.· 0 citations
The teaching-learning process for children with autism spectrum disorder requires structured and context-specific strategies. The objective was to propose pedagogical interventions to strengthen this process at the “América” Educational Unit. An applied, descriptive-propositional study was conducted using a mixed-metho...
A. M. Quiñonez-Saltos, Evelyn Pierina Bautista-Corozo, Rudy García-Cobas et al.· Revista Científica Ciencia y...· 0 citations
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
MIT News · Artificial Intelligence· news.mit.eduOct 13, 2026
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
Microsoft Research Blog· microsoft.comOct 12, 2026
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 7, 2026
Students in MIT’s Concourse program delve deeply into the human condition, debate challenging questions, and learn to develop judgment about issues that can’t be quantified.
Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.
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