The study investigates how to prevent skill loss from relying on AI assistants by testing two interventions. In a preregistered online experiment with 704 participants, metacognitive feedback—explicitly highlighting the consequences of offloading—significantly lowered the likelihood of users offloading answers to an LLM and improved their performance on a subsequent unaided test. No effect was found for an effort-based reward incentive. The findings suggest that metacognitive feedback is an effective design strategy to reduce cognitive offloading to AI.
Reusable skills let LLM agents load task-specific instructions on demand, but they make execution depend on implicit routing over skill metadata. We test whether this design scales by holding SkillsBe...
The paper investigates how to help users monitor their own and an AI system’s competence when using AI assistance. It identifies 30 interventions from experts and organizes them into a design space based on timing, target competence, and source of cue. A large experiment shows that reliability cards and contrasting replies reduce estimation error and overconfidence, though they do not improve task performance.
arXiv:2605. 15850v3 Announce Type: replace-cross Abstract: In recent years, generative AI (GenAI) in educational settings has become ubiquitous in university students' daily lives, despite its potential to induce over-reliance, metacognitive disengagement, and diminished learning when used unrestrictedly.
arXiv:2610.18522v1 Announce Type: new Abstract: Reusable skills let LLM agents load task-specific instructions on demand, but they make execution depend on implicit routing over skill metadata. We te...
arXiv:2606. 15601v1 Announce Type: cross Abstract: We introduce SCAN -- a human-centric decision-making framework to facilitate learners for effective task allocation with Generative Artificial Intelligence (GenAI) based on Vygotsky's Zone of Proximal Development and Metacognition.
As assistants move from reactive answering toward proactive intervention, memory changes role: it no longer only supplies the answer to a query, it determines whether and when to speak at all. Prior w...
The paper introduces Active Preference Acquisition (APA), a method for web agents to decide which unknown user preferences to ask about by evaluating the Prospective Personalization Value (PPV)—the expected cumulative benefit across future tasks minus the cost of asking. APA calculates counterfactual gains for each candidate preference using task prototypes and aggregates them by expected reuse, enabling agents to prioritize questions that may not help immediately but improve long‑term performance. Experiments on the ActiveWebPref benchmark show APA can acquire a large share of valuable preferences and achieve higher net utility than baseline strategies, demonstrating that considering future reuse is crucial for effective personalization.
The paper introduces ProPerMem, a benchmark for memory-driven proactive assistance that pairs egocentric video with long, distractor-rich personal memories and human-verified trigger windows. Baseline models perform poorly, achieving at most 15% accuracy, while an oracle-driven GPT-5.3-Chat model reaches 96% when given evidence inside the trigger window. Building on these insights, the authors develop ProPerAgent, a plug‑and‑play system that retrieves relevant memory from a graph store and uses lightweight reinforcement learning to improve proactive intervention accuracy to 42.4% without training.
The paper introduces MemPABench, an interactive environment designed to evaluate how personal agents remember and act on user interaction preferences. The benchmark features long‑horizon tasks where agents must decide when to clarify, confirm, or act autonomously, using data from four simulated users who provided 99 sessions over four months and five preference changes each. Experiments across four memory systems and LLM backbones show that current memory approaches offer limited improvement in preference selection, with retrieval‑based methods performing no better than no memory and file‑based memory sometimes applying preferences in irrelevant contexts.
The paper introduces Active Preference Acquisition (APA) for long‑term personalized web agents, defining the Prospective Personalization Value (PPV) of a missing preference as its expected cumulative utility over future tasks minus the cost of asking. APA selects questions within a per‑interaction budget, prioritizing preferences that may not benefit the current task but can be reused across many future tasks. Experiments on the ActiveWebPref benchmark show APA acquiring 80.3% of amortization‑dependent preferences and achieving higher net utility than baseline methods, while limiting the number of questions asked.
arXiv:2609.00549v1 Announce Type: new Abstract: Large Language Model (LLM) agents increasingly rely on external skills, yet standard evaluations obscure whether retrieving these skills actually helps...
arXiv:2604. 04721v3 Announce Type: replace Abstract: People often optimize for long-term goals in collaboration: A mentor or companion doesn't just answer questions, but also scaffolds learning, tracks progress, and prioritizes the other person's growth over immediate results.