AI assistants increasingly refer to information they ostensibly retain from earlier sessions. A remembered preference can signal attentiveness, whereas a reference to stress or late-night behavior can feel intrusive. Existing accounts of personalization and privacy do not fully explain why these cues elicit opposing reactions. We treat Memory Boundary Fit (MBF) as a preliminary interaction-specific operationalization of whether remembered information appears to fall within a consumer's contextual boundary for the current exchange. Across six controlled, LLM-delivered chat experiments ( N = 2, 140), we compared three complete simulated message packages that differed in whether and how the assistant referenced remembered information: a relevant-memory cue, an intrusive-memory cue, and no memory reference. The assistant's memory references were fictitious claims rather than genuine retrieval of previously disclosed information. The principal and most consistent finding was that relevant-memory cue packages produced more favorable trust-related outcomes than intrusive-memory cue packages; comparisons with no memory varied across studies. Because the packages also differed in topical relevance, sensitivity, perceived usefulness, information source, and implied surveillance, the studies cannot isolate contextual fit, or any other single cue attribute, as the causal feature, and they do not establish MBF as a distinct psychological mechanism. Study 2 yielded same-session indirect associations consistent with the proposed MBF account. Across Study 2 and a supplemental validation sample, MBF explained additional variance in trust beyond that accounted for by the adjacent appraisals measured alongside it. However, all of these measures were self-reported in the same session. Study 3 found a larger intrusive-memory penalty in the complete sleep-health scenario than in the apparel scenario, but this is an exploratory scenario comparison rather than a test of information sensitivity. In Study 4, the specific editable dashboard tested increased average trust but did not selectively reduce the intrusive-memory penalty. Studies 5 and 6 extended the pattern to data-sharing and relational engagement, as well as to loyalty and opt-in intentions, but not to transactional engagement, which remained unaffected. These findings motivate contextual appropriateness as a candidate design consideration to be tested in real persistent-memory systems.
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.
Zheying Zhang, M. Rayhan, Tomas Herda et al.· International Conference on...· 48 citations· ⚡4
This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.
Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al.· arXiv.org· 44 citations· ⚡2
The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.
Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al.· arXiv.org· 41 citations
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.
AI is moving beyond impressive demos. After World Summit AI 2026 in Amsterdam, GPT-Lab's Muhammad Waseem reflects on what organizations need to make AI agents reliable, responsible and genuinely useful in everyday work. The post AI Has Grown Up. Have We? appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026