Group Cognition and Collaborative AI

Situated Learning: Legitimate Peripheral Participation.

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Situated Learning: Legitimate Peripheral Participation

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"Why Should I Trust You?": Explaining the Predictions of Any Classifier

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The Mythos of Model Interpretability: In machine learning, the concept of interpretability is both important and slippery.

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Grounding in communication.

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A survey of robot learning from demonstration

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Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition

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Apprenticeship learning via inverse reinforcement learning

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Human-level concept learning through probabilistic program induction

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Collaborating: Finding Common Ground for Multiparty Problems

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Referring as a collaborative process

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Distributed cognition

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The Construction of Shared Knowledge in Collaborative Problem Solving.

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Man-Computer Symbiosis

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Multiagent systems: algorithmic, game-theoretic, and logical foundations

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Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission

Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., & Elhadad, N. (2015) · Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2783258.2788613 · Cited by 1,685

A Bayesian computer vision system for modeling human interactions

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Jumping NLP Curves: A Review of Natural Language Processing Research [Review Article]

Cambria, E., & White, B. (2014) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2014.2307227 · Cited by 1,399

A Multiagent Approach to Autonomous Intersection Management

Dresner, K., & Stone, P. (2008) · Journal of Artificial Intelligence Research · doi:10.1613/jair.2502 · Cited by 1,372

Toward a Comprehensive Theory of Collaboration

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Multiagent systems: A survey from a machine learning perspective

Stone, P., & Veloso, M. (2000) · Autonomous Robots · doi:10.1023/a:1008942012299 · Cited by 1,218

Multiagent Systems

Shoham, Y., & Leyton‐Brown, K. (2008) · Cambridge University Press eBooks · doi:10.1017/cbo9780511811654 · Cited by 979

Interactive machine learning for health informatics: when do we need the human-in-the-loop?

Holzinger, A. (2016) · Brain Informatics · doi:10.1007/s40708-016-0042-6 · Cited by 906

Artificial intelligence and tutoring systems: Computational and cognitive approaches to the communication of knowledge

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Real conversations with artificial intelligence: a comparison between human–human online conversations and human–chatbot conversations

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Reflections on shared cognition

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Elements of a Plan‐Based Theory of Speech Acts*

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Human–Robot Interaction: Status and Challenges

Sheridan, T. B. (2016) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720816644364 · Cited by 786

Rationalizing Neural Predictions

Lei, T., Barzilay, R., & Jaakkola, T. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1011 · Cited by 777

Elements of a plan-based theory of speech acts

Cohen, P. R., & Perrault, C. R. (1979) · Cognitive Science · doi:10.1016/s0364-0213(79)80006-3 · Cited by 733

Building Intelligent Interactive Tutors : student-centered strategies for revolutionizing e-learning

Woolf, B. P. (2009) · HAL · Cited by 716

Group Cognition

Stahl, G. (2006) · The MIT Press eBooks · doi:10.7551/mitpress/3372.001.0001 · Cited by 695

Collaborative Leadership: How Citizens and Civic Leaders Can Make a Difference

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Intellectual Teamwork: Social and Technological Foundations of Cooperative Work.

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Principles of Explanatory Debugging to Personalize Interactive Machine Learning

Kulesza, T., Burnett, M., Wong, W.-K., & Stumpf, S. (2015) · Proceedings of the 20th International Conference on Intelligent User Interfaces · doi:10.1145/2678025.2701399 · Cited by 576

Anticipating Visual Representations from Unlabeled Video

Vondrick, C., Pirsiavash, H., & Torralba, A. B. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.18 · Cited by 538

Generating Visual Explanations

Hendricks, L. A., Akata, Z., Rohrbach, M., Donahue, J., Schiele, B., & Darrell, T. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_1 · Cited by 534

Theory-of-Mind Development: Retrospect and Prospect

Flavell, J. H. (2004) · Merrill-palmer Quarterly · doi:10.1353/mpq.2004.0018 · Cited by 497

Supporting Social Interaction in an Intelligent Collaborative Learning SystemPreprint

Soller, A. (2001) · HAL (Le Centre pour la Communication Scientifique Directe) · Cited by 488

Jumping NLP Curves: A Review of Natural Language Processing Research

Cambria, E., & White, B. (2014) · Cited by 455

A review of verbal and non-verbal human–robot interactive communication

Mavridis, N. (2015) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2014.09.031 · Cited by 453

Using genetic algorithms for concept learning

Jong, K. A. D., Spears, W. M., & Gordon, D. F. (1993) · Machine Learning · doi:10.1007/bf00993042 · Cited by 445

Mixed-initiative interaction

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Untitled work

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Reading the Mind in the Eyes or Reading between the Lines? Theory of Mind Predicts Collective Intelligence Equally Well Online and Face-To-Face

Engel, D., Woolley, A. W., Jing, L. X., Chabris, C. F., & Malone, T. W. (2014) · PLoS ONE · doi:10.1371/journal.pone.0115212 · Cited by 339

Human Computing and Machine Understanding of Human Behavior: A Survey

Pantić, M., Pentland, A., Nijholt, A., & Huang, T. S. (2007) · Springer eBooks · doi:10.1007/978-3-540-72348-6_3 · Cited by 320

An Evidence-Based Approach to Interactive Health Communication A Challenge to Medicine in the Information Age

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Learning Bayesian Belief Network Classifiers: Algorithms and System

Cheng, J., & Greiner, R. (2001) · Lecture notes in computer science · doi:10.1007/3-540-45153-6_14 · Cited by 289

Real-Time Inference of Complex Mental States from Facial Expressions and Head Gestures

El Kaliouby, R., & Robinson, P. (2005) · 2004 Conference on Computer Vision and Pattern Recognition Workshop · doi:10.1109/cvpr.2004.427 · Cited by 282

CROSS-UNDERSTANDING: IMPLICATIONS FOR GROUP COGNITION AND PERFORMANCE.

HUBER, G. P., & Lewis, K. (2010) · Academy of Management Review · doi:10.5465/amr.2010.45577787 · Cited by 269

Cognitive Diversity and Consensus in Group Decision Making: The Role of Inputs, Processes, and Outcomes

Mohammed, S., & Ringseis, E. L. (2001) · Organizational Behavior and Human Decision Processes · doi:10.1006/obhd.2000.2943 · Cited by 267

Recognizing engagement in human-robot interaction

Rich, C., Ponsler, B., Holroyd, A., & Sidner, C. L. (2010) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1109/hri.2010.5453163 · Cited by 266

The role of grounding in collaborative learning tasks

Baker, M. J., Hansen, T. G. B., Joiner, R., & Traum, D. R. (1999) · Cited by 264

Recognizing group cognition

Theiner, G., Allen, C., & Goldstone, R. L. (2010) · Cognitive Systems Research · doi:10.1016/j.cogsys.2010.07.002 · Cited by 231

Intellectual Teamwork

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How people anthropomorphize robots

Fussell, S. R., Kiesler, S. B., Setlock, L. D., & Yew, V. (2008) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/1349822.1349842 · Cited by 209

Apolo

Chau, D. H., Kittur, A., Hong, J., & Faloutsos, C. (2011) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/1978942.1978967 · Cited by 190

Collaboration in collaborative learning

Baker, M. J. (2015) · Interaction Studies Social Behaviour and Communication in Biological and Artificial Systems · doi:10.1075/is.16.3.05bak · Cited by 153

Interactive health communication in preventive medicine. Internet-based strategies in teaching and research

Fotheringham, M. (2000) · American Journal of Preventive Medicine · doi:10.1016/s0749-3797(00)00188-4 · Cited by 152

Reconsidering group cognition: From conceptual confusion to a boundary area between cognitive and socio-cultural perspectives?

Akkerman, S. F., den Bossche, P. V., Admiraal, W., Gijselaers, W., Segers, M. S. R., Simons, R., & Kirschner, P. A. (2007) · Educational Research Review · doi:10.1016/j.edurev.2007.02.001 · Cited by 144

Natural Interface Using Pointing Behavior for Human–Robot Gestural Interaction

Sato-Shimokawara, E., Yamaguchi, T., & Harashima, F. (2007) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2007.892728 · Cited by 135

Real-Time Inference of Complex Mental States from Facial Expressions and Head Gestures

el Kaliouby, R., & Robinson, P. (2005) · Springer eBooks · doi:10.1007/0-387-27890-7_11 · Cited by 134

Wirtschaftsinformatik

Heinrich, L. J., Heinzl, A., & Riedl‬, R. (2010) · Springer-Lehrbuch · doi:10.1007/978-3-642-15426-3 · Cited by 124

Mixed-Initiative Creative Interfaces

Deterding, S., Hook, J., Fiebrink, R., Gillies, M., Gow, J., Akten, M., Smith, G., Liapis, A., & Compton, K. (2017) · Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems · doi:10.1145/3027063.3027072 · Cited by 117

The effect of head-nod recognition in human-robot conversation,

Sidner, C. L., Lee, C., Morency, L.-P., & Forlines, C. (2006) · Proceedings of the 1st ACM SIGCHI/SIGART conference on Human-robot interaction · doi:10.1145/1121241.1121291 · Cited by 107

A methodological approach relating the classification of gesture to identification of human intent in the context of human-robot interaction

Nehaniv, C. L., Dautenhahn, K., Kubacki, J., Hægele, M., Parlitz, C., & Alami, R. (2005) · IEEE RO-MAN · doi:10.1109/roman.2005.1513807 · Cited by 105

Towards interactive Machine Learning (iML): Applying Ant Colony Algorithms to Solve the Traveling Salesman Problem with the Human-in-the-Loop Approach

Holzinger, A., Plass, M., Holzinger, K., Crişan, G. C., Pintea, C.-M., & Palade, V. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-45507-5_6 · Cited by 98

Responsive robot gaze to interaction partner

Yoshikawa, Y., Shinozawa, K., Ishiguro, H., Hagita, N., & Miyamoto, T. (2006) · Robotics: Science and Systems · doi:10.15607/rss.2006.ii.037 · Cited by 95

Multi-agent role allocation: issues, approaches, and multiple perspectives

Campbell, A., & Wu, A. S. (2010) · Autonomous Agents and Multi-Agent Systems · doi:10.1007/s10458-010-9127-4 · Cited by 87

The Future of Disaster Response: Humans Working with Multiagent Teams using DEFACTO.

Schurr, N., Marecki, J., Tambe, M., Scerri, P., Kasinadhuni, N., & Lewis, J. P. (2005) · Cited by 80

Artificial Intelligence and Creativity: An Interdisciplinary Approach

Dartnall, T. (1994) · Cited by 78

Concurrent Plan Recognition and Execution for Human-Robot Teams

Levine, S. A., & Williams, B. C. (2014) · Proceedings of the International Conference on Automated Planning and Scheduling · doi:10.1609/icaps.v24i1.13672 · Cited by 74

Mind reading machines: automated inference of cognitive mental states from video

el Kaliouby, R., & Robinson, P. (2005) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.2004.1398380 · Cited by 70

Teacher-education students’ views about knowledge building theory and practice

Hong, H., Chen, F.-C., Chai, C. S., & Chan, W.-C. (2010) · Instructional Science · doi:10.1007/s11251-010-9143-4 · Cited by 51

A POMDP framework for modelling human interaction with assistive robots

Taha, T., Miró, J. V., & Dissanayake, G. (2011) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2011.5980323 · Cited by 40

Predicting Motivations of Actions by Leveraging Text

Vondrick, C., Oktay, D., Pirsiavash, H., & Torralba, A. B. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.327 · Cited by 38

From Intersubjectivity to Group Cognition

Stahl, G. (2016) · Computer Supported Cooperative Work (CSCW) · doi:10.1007/s10606-016-9243-z · Cited by 36

The Messages of Mute Machines: Human-Machine Communication with Industrial Technologies

Guzman, A. L. (2016) · Scholarworks (University of Massachusetts Amherst) · doi:10.7275/r57p8wbw · Cited by 35

Human-robot collaboration for a shared mission

Karami, A., Jeanpierre, L., & Mouaddib, A. (2010) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1109/hri.2010.5453219 · Cited by 35

The role of cognitive abilities in children's inferences about social atypicality and peer exclusion and inclusion in intergroup contexts

Abrams, D., Rutland, A., Palmer, S. B., Pelletier, J., Ferrell, J. M., & Lee, S. M. (2014) · British Journal of Developmental Psychology · doi:10.1111/bjdp.12034 · Cited by 33

The multiple, interacting levels of cognitive systems (MILCS) perspective on group cognition

Goldstone, R. L., & Theiner, G. (2017) · Philosophical Psychology · doi:10.1080/09515089.2017.1295635 · Cited by 31

Recognizing engagement in human-robot interaction

Rich, C., Ponsleur, B., Holroyd, A., & Sidner, C. L. (2010) · doi:10.5555/1734454.1734580 · Cited by 31

Reasoning Under Uncertainty: Towards Collaborative Interactive Machine Learning

Robert, S., Büttner, S., Röcker, C., & Holzinger, A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-50478-0_18 · Cited by 28

An Open-Source Dialog System with Real-Time Engagement Tracking for Job Interview Training Applications

Zhou, Y., Ramanarayanan, V., Lange, P. L., & Suendermann‐Oeft, D. (2018) · Lecture notes in electrical engineering · doi:10.1007/978-3-319-92108-2_21 · Cited by 27

Mine, yours... ours? Designing for principal-agent collaboration in interactive value creation

Novak, J. (2009) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-25263 · Cited by 26

Design implications for Designing with a Collaborative AI

Koch, J. (2017) · National Conference on Artificial Intelligence · Cited by 24

Internet of Things integration to a Multi Agent System based manufacturing environment

Alexakos, C., & Kalogeras, A. P. (2015) · IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) · doi:10.1109/etfa.2015.7301415 · Cited by 21

Making machine learning useable

Zhou, J. J., & Chen, F. (2015) · International Journal of Intelligent Systems Technologies and Applications · doi:10.1504/ijista.2015.074069 · Cited by 20

Deep networks for predicting human intent with respect to objects

Kelley, R., Wigand, L., Hamilton, B., Browne, K., Nicolescu, M. N., & Nicolescu, M. (2012) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/2157689.2157740 · Cited by 19

Active visual sensing and collaboration on mobile robots using hierarchical POMDPs

Zhang, S., & Sridharan, M. (2012) · doi:10.5555/2343576.2343602 · Cited by 16

ICE: Enabling Non-Experts to Build Models Interactively for Large-Scale Lopsided Problems

Simard, P., Chickering, D., Lakshmiratan, A., Charles, D., Bottou, L., Suarez, C. G. J., Grangier, D., Amershi, S., Verwey, J., & Suh, J. (2014) · arXiv · doi:10.48550/arxiv.1409.4814 · Cited by 15

Detection of social signals for recognizing engagement in human-robot interactionPreprint

Lala, D., Inoue, K., Milhorat, P., & Kawahara, T. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.10257 · Cited by 15

Human computing, virtual humans and artificial imperfection

Ruttkay, Z., Reidsma, D., & Nijholt, A. (2006) · International Conference on Multimodal Interfaces · doi:10.1145/1180995.1181033 · Cited by 14

Character-Net: Character Network Analysis from Video

Park, S.-B., Kim, Y., Uddin, M. N., & Jo, G. (2009) · IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) · doi:10.1109/wi-iat.2009.54 · Cited by 12

User-Agent Value Alignment

Shapiro, D. G., & Shachter, R. D. (2002) · Cited by 12

Elements of a Plan-Based Theory of Speech Acts. Technical Report No. 141.

Cohen, P. R., & Perrault, C. R. (1979) · Cited by 9

Cognitive state recognition using wavelet singular entropy and ARMA entropy with AFPA optimized GP classification

Cai, Z., Wu, E. Q., Huang, D., Ding, L., Yu, B., Law, R., Huang, J., & Fu, S. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.01.054 · Cited by 9

Collaborative machine scheduling: Challenges of individually optimizing behavior

Lang, F. U., & Fink, A. (2014) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.3425 · Cited by 5

Dialog act modeling for virtual personal assistant applications using a small volume of labeled data and domain knowledge

Lee, D., Lee, J., Kim, E.-K., & Lee, J. (2015) · Interspeech (USB) · doi:10.21437/interspeech.2015-310 · Cited by 3

Mediated Cognition

Turner, P. (2016) · Human-computer interaction series · doi:10.1007/978-3-319-42235-0_2 · Cited by 1

From explainable to interactive AI: A literature review on current trends in human-AI interaction

Raees, M., Meijerink, I., Lykourentzou, I., Khan, V.-J., & Papangelis, K. (2024) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2024.103301 · Cited by 180

Keeping the organization in the loop: a socio-technical extension of human-centered artificial intelligence

Herrmann, T., & Pfeiffer, S. (2023) · AI & SOCIETY · doi:10.1007/s00146-022-01391-5 · Cited by 173

Exploring Challenges and Opportunities to Support Designers in Learning to Co-create with AI-based Manufacturing Design Tools

Gmeiner, F., Yang, H., Yao, L., Holstein, K., & Martelaro, N. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580999 · Cited by 130

Designing Deep Reinforcement Learning for Human Parameter Exploration

Scurto, H., Kerrebroeck, B. V., Caramiaux, B., & Bevilacqua, F. P. (2021) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3414472 · Cited by 33

AI as a user of AI: Towards responsible autonomy

Shukla, A. K., Terziyan, V. Y., & Tiihonen, T. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e31397 · Cited by 20

"Learn, Use, and (Intermittently) Abandon": Exploring the Practices of Early Smart Speaker Adopters in Urban India

Garg, R., Cui, H., & Kapadia, Y. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3479495 · Cited by 15

An Evidence-based Workflow for Studying and Designing Learning Supports for Human-AI Co-creation

Gmeiner, F., Conlin, J. L., Tang, E. H., Martelaro, N., & Holstein, K. (2024) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3613905.3650763 · Cited by 14

Governance of Collaborative AI Development Strategies

Wiesmüller, S., & Bauer, M. (2023) · CSR, sustainability, ethics & governance · doi:10.1007/978-3-031-09245-9_4 · Cited by 4

Promoting the Adoption of AI-Based Recommendations Through Organizational Practices

Herrmann, T. A., & Nolte, A. (2024) · Lecture notes in information systems and organisation · doi:10.1007/978-3-031-76970-2_13 · Cited by 3

Sztuczna inteligencja (AI) jako megatrend kształtujący edukację. Jak przygotowywać się na szanse i wyzwania społeczno-gospodarcze związane ze sztuczną inteligencją?

Będkowski, M., Brdulak, J., Fazlagić, J., Janiszewski, M., Jemielniak, D., Kornowska, L., Piekarski, C., Przegalińska, A., Rabiega-Wiśniewska, J., Stęchły, J., et al. (2022) · Instytut Badań Edukacyjnych eBooks · doi:10.24131/9788367385107 · Cited by 2

A hypotheses-driven framework for human–machine expertise process

Sounchio, S. S., Geneste, L., & Kamsu-Foguem, B. (2024) · Cognitive Systems Research · doi:10.1016/j.cogsys.2024.101255

Analyse der Mensch-KI-Interaktion – Vertrauen, Akzeptanz und der Human-in-the-Loop-Ansatz

von Garrel, J. (2025) · Springer eBooks · doi:10.1007/978-3-658-49275-5_4

Designing With Machine Learning for Interactive Music Dispositifs

Scurto, H. (2019) · theses.fr (ABES) · doi:10.70675/1bf87ea2z8d0cz4c5eza982zdb110f9744bb

Modelling and exploitation of knowledge for collaborative expertise processes

Sounchio, S. S. (2022) · doi:10.70675/cc1be2ebz89bdz42d7z8b65zcbfd8c577a84

AI effect on innovation capacity in the context of industry 5.0: A Bayesian network analysis

Bécue, A., da Gama, J. M. P., & Brito, P. Q. (2026) · Strategic Business Research · doi:10.1016/j.sbr.2026.100154

Collaborative Cognition: Emergence Through Sustained Human–AI InteractionPreprint

Skulski, A. (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.21315853

Giving AI a Theory of Mind

Eicher, B. L. (2018) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3278721.3278797 · Cited by 1

Collaborative Commonsense AI

Ilievski, F. (2024) · Synthesis lectures on computer science · doi:10.1007/978-3-031-69974-0_3

The Role of Adaptation in Collective Human–AI Teaming

Zhao, M., Simmons, R. G., & Admoni, H. (2022) · Topics in Cognitive Science · doi:10.1111/tops.12633 · Cited by 48

Toward Collaborative AI: A Framework for the Transition from Autonomous Agents to Adaptive Human-AI Partners

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