The Design Space of Recent AI-assisted Research Tools for Ideation, Sensemaking, and Scientific Creativity
Bibliographic record
Abstract
Generative AI (GenAI) tools are radically expanding the scope and capability of automation in knowledge work such as academic research. While promising for augmenting cognition and streamlining processes, AI-assisted research tools may also increase automation bias and hinder critical thinking. To examine recent developments, we surveyed publications from leading HCI venues over the past three years, closely analyzing thirteen tools to better understand the novel capabilities of these AI-assisted systems and the design spaces they enable: seven employing traditional AI or customized transformer-based approaches, and six integrating open-access large language models (LLMs). Our analysis characterizes the emerging design space, distinguishes between tools focused on workflow mimicry versus generative exploration, and yields four critical design recommendations to guide the development of future systems that foster meaningful cognitive engagement: providing user agency and control, differentiating divergent/convergent thinking support, ensuring adaptability, and prioritizing transparency/accuracy. This work discusses how these insights signal a shift from mere workflow replication towards generative co-creation, presenting new opportunities for the community to craft intuitive, AI-driven research interfaces and interactions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.034 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.020 | 0.016 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".