Collective Intelligence

Intersecting, urgent challenges are precipitating large-scale crises, ecological, democratic, military, health and educational, to name just a few. This complexity is overwhelming our sensemaking capacity, provoking deep reflection across government, business, civic society and the academy. Fields as diverse and intersecting as organisation science, cognitive science, computer science and neuroscience are converging on the importance of Collective Intelligence (CI), ranging in scale from small teams to companies, to global networks (Malone & Bernstein, 2015).

In the editorial to the inaugural edition of CI journal, Flack et al. (2022) introduce CI as follows:

“We can find collective intelligence in any system in which entities collectively, but not necessarily cooperatively, act in ways that seem intelligent. Often—but not always—the group’s intelligence is greater than the intelligence of individual entities in the collective.” They further suggest that we have recently witnessed “two epic collective intelligence failures: the responses to COVID and climate change”. 

Hybrid Collective Intelligence

In our digitally enabled, connected world, it is natural that CI constitutes more than the collective ability of human minds. Computational platforms make new forms of discourse and coordination possible (De Liddo et al., 2012; Iandoli et al., 2016; Suran et al., 2020; van Gelder et al., 2020; Gupta et al., 2023). Artificial Intelligence (AI) adds machine actors to the network, with human-agent teaming research clarifying the conditions under which professionals come to trust AI agents as members of the team (O’Neill et al., 2022; Seeber et al., 2020). The explosive arrival of large language models combined with conversational user interfaces is the most recent technical advance, opening new possibilities for human-computer creativity and sensemaking (Rick et al., 2023). Such developments in our sociotechnical knowledge infrastructure lead Gupta et al. (2023) to ask:

“How do we know that such a sociotechnical system as a whole, consisting of a complex web of hundreds of human–machine interactions, is exhibiting CI?” and argue for sociotechnical architectures capable of sustaining “collective memory, attention, and reasoning”.

The challenge for Higher Education

It seems uncontroversial to argue that citizens should be equipped for this new world, but to date, it is unclear what it means to educate for CI (Hogan et al., 2023). Schuler (2014) argues for an explicitly moral dimension to CI, and proposes a set of capacities underpinning “civic intelligence”. These strands of work are concerned not only with educating people about CI, but cultivating their ability to engage in CI practices, and increase CI capacity.

We are therefore convening this symposium to develop the conversation, to forge foundational concepts, and build the network needed to advance this agenda across diverse boundaries. We invite submissions addressing questions such as the following (non-exhaustive) deeply interwoven perspectives:

Theoretical

• How  does educating for CI fit into other emerging conceptions of the future of education?
• How should we conceive collective learning in relation to CI?
• Are there key dimensions, and tradeoffs, when educating for CI?
• How does the way we conceptualise technologies, intelligence and collectivity align with how we conceptualise CI?

Pedagogical

• What knowledge, skills and dispositions do graduates need for CI?
• What does a developmental trajectory in CI learning look like?
• What pedagogies cultivate CI?
• How do we assess CI?
• How can CI counterbalance the individual focus of education, particularly assessment?
• How can conversational AI support the development of critical thinking and collaboration skills in CI?
• In what ways can conversational AI act as a facilitator in educational settings to enhance CI and participatory learning?

Technological

• What is the design space of educational technologies for CI? (and how does this differ from CI technology for professionals?)
• What is the design rationale for a given platform to support educating for CI?
• How are such platforms used by students?
• What roles can AI play in enhancing CI education?

Ethical

• What are the ethical implications of using AI-enabled CI, and how do students learn ethical practices?

Key dates

• Sept 15th 11.59pm AOE: Submit a position statement if you wish to propose a 15min talk:
  • Max 4 pages (12pt Times including figs/refs)
  • Queries to Simon.BuckinghamShum {at} uts.edu.au
• Oct 25th AOE: Notification of acceptance
• Dec 5th 12-3pm PST =  8-11pm GMT = Dec 6th 7-10am AEDT: Online Symposium

Chairs

• Simon Buckingham Shum (University of Technology Sydney, AUS)
• Rupert Wegerif (University of Cambridge, UK)
• Michael Hogan (University of Galway, IRE)
• Margaret Bearman (Deakin University, AUS)
• Baki Kocaballi (University of Technology Sydney, AUS)
• Anna De Liddo (The Open University, UK)

Register

References

De Liddo, A., Sándor, Á., & Buckingham Shum, S. (2012). Contested Collective Intelligence: Rationale, Technologies, and a Human-Machine Annotation Study. Computer Supported Cooperative Work, 21(4-5), 417-448. https://doi.org/http://dx.doi.org/doi:10.1007/s10606-011-9155-x 

Flack, J., Ipeirotis, P., Malone, T. W., Mulgan, G., & Page, S. E. (2022). Editorial to the Inaugural Issue of Collective Intelligence. Collective Intelligence, 1(1), 1-3. https://doi.org/10.1177/26339137221114179 

Gupta, P., Nguyen, T. N., Gonzalez, C., & Woolley, A. W. (2023). Fostering Collective Intelligence in Human–AI Collaboration: Laying the Groundwork for COHUMAIN. Topics in Cognitive Science, (Online: 29 June 2023), 1-28. https://doi.org/10.1111/tops.12679

Hogan, M. J., Barton, A., Twiner, A., James, C., Ahmed, F., Casebourne, I., Steed, I., Hamilton, P., Shi, S., Zhao, Y., Harney, O. M., & Wegerif, R. (2023). Education for collective intelligence. Irish Educational Studies,  (Online: 5 Sept. 2023), 1-30. https://doi.org/10.1080/03323315.2023.2250309 

Iandoli, L., Quinto, I., De Liddo, A., & Buckingham Shum, S. (2016). On online collaboration and construction of shared knowledge: Assessing mediation capability in computer supported argument visualization tools. Journal of the Association for Information Science and Technology, 67(5), 1052-1067. https://doi.org/https://doi.org/10.1002/asi.23481

Klein, M. (2012). Enabling Large-Scale Deliberation Using Attention-Mediation Metrics. Computer-Supported Cooperative Work, 21(4–5), 449–473. https://doi.org/10.1007/s10606-012-9156-4

Malone, T. W., & Bernstein, M. S. (2015). Handbook of Collective Intelligence. The MIT Press. https://cci.mit.edu/cichapterlinks/

O’Neill, T., McNeese, N., Barron, A., & Schelble, B. (2022). Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature. Human Factors, 64(5), 904-938. https://doi.org/10.1177/0018720820960865 

Rick, S. R., Giacomelli, G., Wen, H., Laubacher, R. J., Taubenslag, N., Heyman, J. L., Knicker, M. S., Jeddi, Y., Maier, H., Dwyer, S., Ragupathy, P., & Malone, T. W. (2023). Supermind Ideator: Exploring generative AI to support creative problem-solving. https://doi.org/10.48550/arXiv.2311.01937

Schuler, D. (2014). Pieces of Civic Intelligence: Towards a Capacities Framework. E-Learning and Digital Media, 11(5), 518-529. https://doi.org/10.2304/elea.2014.11.5.518

Seeber, I., Bittner, E., Briggs, R. O., de Vreede, T., de Vreede, G.-J., Elkins, A., Maier, R., Merz, A. B., Oeste-Reiß, S., Randrup, N., Schwabe, G., & Söllner, M. (2020). Machines as teammates: A research agenda on AI in team collaboration. Information & Management, 57(2), 103174. https://doi.org/https://doi.org/10.1016/j.im.2019.103174 

Suran, S., Pattanaik, V., & Draheim, D. (2020). Frameworks for Collective Intelligence: A Systematic Literature Review. ACM Computing Surveys, 53(1), Article 14. https://doi.org/10.1145/3368986

van Gelder, T., Kruger, A., Thomman, S., De Rozario, R., Silver, E., Saletta, M., Barnett, A., Sinnott, R. O., Jayaputera, G. T., & Burgman, M. (2020). Improving Analytic Reasoning via Crowdsourcing and Structured Analytic Techniques. Journal of Cognitive Engineering and Decision Making, 14(3), 195-217. https://doi.org/10.1177/1555343420926287