Man-machine Collaborative Problem-solving

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In a previous thread (and another), we discussed that AI systems could serve groups of people as Internet forum moderators and facilitators.

One can also view AI systems as products or services capable of performing real-time “chores” for teams of people performing tasks in forums such as collaborative problem-solving activities for society.

Today, such capabilities include, but are not limited to: question-answering, fact-checking, automated research, and multimedia content generation (charts, figures, graphs, infographics, tables). Imaginably, over the course of time, these capabilities will only increase in complexity, intricacy, and sophistication.

With AI technologies, teams can solve more problems for society more rapidly. Towards measurably delivering these capabilities, how should scientists analyze and evaluate man-machine collaborative-problem-solving from transcripts and/or Internet forum data? With respect to education, how should we best integrate these findings to teach and assess man-machine collaborative problem-solving skills?

Also, in my opinion, man-machine collaborative problem-solving capabilities should be made available not only to our businesspeople, scholars, and scientists, but to our policy thinktanks, elected officials, committees, and their staffs, in places where collaborative problem-solving coincides with political power, the power to realize and implement our best solutions.

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Here is a relevant The New York Times article: ChatGPT, Other Chatbots Approved for Official Use in the Senate.

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pretty sure i have this

working on the neoj parts , the traffic cameras, also the city planning suprisingly annoying.
but this feeds into other systems I have, for buisness, edu, I didnt want to add the TOR aspects yet.

let me know if you wanna collab bro bro. im a good friend to have

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Hello. Yes, there are many exciting opportunities with respect to enabling, evaluating, and enhancing man-machine collaborative problem-solving, both in general and with respect to its public-sector applications. I’m open to collaboration.

Regarding the evaluation of data (traces, event logs, transcripts, and forum data), I consider there to be three possibilities: using AI (e.g., LLM as judge), using other techniques, and using hybrid approaches combining AI with other techniques.

LLM as Judge

Other Tools

With Copilot, I put together an initial list of other techniques which can be used individually or in combination: epistemic network analysis (ENA), discrete time Markov chains (DTMCs), synergy degree model (SDM), ordered network analysis (ONA), multidimensional sequence analysis, group metacognition measures, interaction analysis model (IAM) framework, and connectivist interaction and engagement (CIE) framework.

Selected Bibliography

Adler, E. Scott, and John D. Wilkerson. Congress and the politics of problem solving. Cambridge University Press, 2013.

Brandl, Laura, Constanze Richters, Nicola Kolb, and Matthias Stadler. “Can generative artificial intelligence ever be a true collaborator? Rethinking the nature of collaborative problem-solving.” In 2nd Workshop on Generative AI for Learning Analytics (GenAI-LA), pp. 80-87. 2025.

Cheruiyot, Langat Gilbert, and Gyöngyvér Molnár. “Technology-based assessment of collaborative problem-solving skills: A bibliometric analysis and review.” Journal of Computers in Education (2025): 1-22.

Graesser, Arthur C., Stephen M. Fiore, Samuel Greiff, Jessica Andrews-Todd, Peter W. Foltz, and Friedrich W. Hesse. “Advancing the science of collaborative problem solving.” Psychological science in the public interest 19, no. 2 (2018): 59-92.

He, Qiwei, Matthias von Davier, Samuel Greiff, Eric W. Steinhauer, and Paul B. Borysewicz. “Collaborative problem solving measures in the Programme for International Student Assessment (PISA).” In Innovative assessment of collaboration, pp. 95-111. Cham: Springer International Publishing, 2017.

Mayhew, David R. “Congress as problem solver.” Promoting the general welfare: New perspectives on government performance (2006): 219-36.