Advait Sarkar

1. Computer-Supported Collaborative Learning (CSCL) is a branch of the learning sciences that studies how students can learn together with the support of computers (Stahl et al., 2006). Within CSCL...

“When should Generative AI (GenAI) assistants intervene, if at all?” While current GenAI assistants in knowledge work primarily operate reactively, responding only when users initiate an interaction, there is significant potential for these assistants to proactively assist in tasks. To tap into the potential of proactive GenAI assistants, it is important to examine when interventions are useful, when they are harmful, and how such judgements can be made reliably. The goal of this workshop, TimelyAI, at CHIWORK 2026 is to bring together Human-Computer Interaction (HCI) researchers interested in AI, psychological science, and social science to examine the timing of GenAI intervention in knowledge work and establish a shared research agenda.

Building on the first Tools for Thought (TfT) workshop at CHI 2025, we invite researchers, designers, and practitioners to further operationalise approaches for the design, usage, and evaluation of Generative Artificial Intelligence (GenAI) as a TfT. The first goal of the workshop is to put into focus which outcomes a TfT should help people achieve to effectively augment their cognition while avoiding its erosion. Secondly, we will explore how to achieve these outcomes through design and usage strategies. Third, the workshop will also address what a TfT needs for its successful adoption and integration into people’s flow, so that they can benefit from the tools’ potential in their own terms. By focussing on these three research goals, the workshop aims to further develop and advance the multidisciplinary TfT community interested in exploring research frameworks, theories, methods, and approaches to conceptualising, designing, and researching GenAI as a TfT.

Generative AI (GenAI) tools improve productivity in knowledge workflows such as writing, but also risk overreliance and reduced critical thinking. Cognitive forcing functions (CFFs) mitigate these risks by requiring active engagement with AI output. As GenAI workflows grow more complex, systems increasingly present execution plans for user review. However, these plans are themselves AI-generated and prone to overreliance, and the effectiveness of applying CFFs to AI plans remains underexplored. We conduct a controlled experiment in which participants completed AI-assisted writing tasks while reviewing AI-generated plans under four CFF conditions: Assumption (argument analysis), WhatIf (hypothesis testing), Both, and a no-CFF control. A follow-up think-aloud and interview study qualitatively compared these conditions. Results show that the Assumption CFF most effectively reduced overreliance without increasing cognitive load, while participants perceived the WhatIf CFF as most helpful. These findings highlight the value of plan-focused CFFs for supporting critical reflection in GenAI-assisted knowledge work.

Spreadsheet collaboration provides valuable opportunities for learning and expertise sharing between colleagues. Sharing expertise is essential for the retention of important technical skillsets within organisations, but previous studies suggest that spreadsheet experts often fail to disseminate their knowledge to others. We suggest that social norms and beliefs surrounding the value of spreadsheet use significantly influence user engagement in sharing behaviours. To explore this, we conducted 31 semi-structured interviews with professional spreadsheet users from two separate samples. We found that spreadsheet providers face challenges in adapting highly personalised strategies to often subjective standards and in evaluating the appropriate social timing of sharing. In addition, conflicted self-evaluations of one's spreadsheet expertise, dismissive normative beliefs about the value of this knowledge, and concerns about the potential disruptions associated with collaboration can further deter sharing. We suggest that these observations reflect the challenges of long-term learning in feature-rich software designed primarily with initial learnability in mind. We therefore provide implications for design to navigate this tension. Overall, our findings demonstrate how the complex interaction between technology design and social dynamics can shape collaborative learning behaviours in the context of feature-rich software.