Aakriti Kumar
Impact in
- Health Informatics top 5%
- Artificial Intelligence in Healthcare and Education
Papers in
-
- Explainable Artificial Intelligence (XAI) 6
- Intelligent Tutoring Systems and Adaptive Learning 1
- AI-based Problem Solving and Planning 1
-
- Ethics and Social Impacts of AI 3
- Co-authors
- Mark Steyvers (8 shared papers)Padhraic Smyth (4 shared papers)Heliodoro Tejeda (2 shared papers)Julia M. Haaf (1 shared paper)Jeffrey N. Rouder (1 shared paper)Aaron S. Benjamin (2 shared papers)Andrew Heathcote (1 shared paper)Jessica Hullman (1 shared paper)
- Journals
- Nature Machine Intelligence (1 paper)npj Science of Learning (1 paper)Psychological Review (1 paper)Perspectives on Psychological Science (1 paper)Psychonomic Bulletin & Review (1 paper)
- Partner nations
- United StatesNetherlandsAustralia
In The Last Decade
Aakriti Kumar
9 papers receiving 188 citations
Aakriti Kumar's Hit Papers
Peers
Comparison fields: 5 of 59
- Health Informatics 29
- General Decision Sciences 7
- Safety Research 31
- Artificial Intelligence 75
- Cognitive Neuroscience 26
Countries citing papers authored by Aakriti Kumar
This map shows the geographic impact of Aakriti Kumar's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Aakriti Kumar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Aakriti Kumar more than expected).
Fields of papers citing papers by Aakriti Kumar
This network shows the impact of papers produced by Aakriti Kumar. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Aakriti Kumar. The network helps show where Aakriti Kumar may publish in the future.
Co-authors
The 12 scholars most cited alongside Aakriti Kumar, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2023 | 71 | |
| 2 | What large language models know and what people think they know Hit paper breakdown → | 2025 | 44 |
| 3 | 2023 | 30 | |
| 4 | 2022 | 28 | |
| 5 | 2023 | 11 | |
| 6 | Explaining Algorithm Aversion with Metacognitive Bandits | 2021 | 4 |
| 7 | 2022 | 4 | |
| 8 | 2023 | 2 | |
| 9 | 2025 | 1 | |
| 10 | 2023 | 0 |
About Aakriti Kumar
Aakriti Kumar is a scholar working on Artificial Intelligence, Safety Research, Health Informatics, General Decision Sciences and Computer Networks and Communications, having authored 10 papers that have together received 195 indexed citations. Recurring topics across this work include Explainable Artificial Intelligence (XAI) (6 papers), Ethics and Social Impacts of AI (3 papers), Artificial Intelligence in Healthcare and Education (2 papers), Decision-Making and Behavioral Economics (2 papers), Intelligent Tutoring Systems and Adaptive Learning (1 paper), AI-based Problem Solving and Planning (1 paper), Autonomous Vehicle Technology and Safety (1 paper) and Advanced Vision and Imaging (1 paper). The work is most often cited by research in Health Informatics (29 citations), General Decision Sciences (7 citations), Safety Research (31 citations), Artificial Intelligence (75 citations) and Cognitive Neuroscience (26 citations). Aakriti Kumar has collaborated with scholars based in United States, Netherlands and Australia. Frequent co-authors include Mark Steyvers, Padhraic Smyth, Heliodoro Tejeda, Julia M. Haaf, Jeffrey N. Rouder, Aaron S. Benjamin, Andrew Heathcote, Jessica Hullman, Teruhisa Misu and Matthew Groh. Their work appears in journals such as Nature Machine Intelligence, npj Science of Learning, Psychological Review, Perspectives on Psychological Science and Psychonomic Bulletin & Review.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.