Harnessing Digital Media in Education and Society

Generative AI: Implications and Applications for Education: summary with timestamps here.

About the Cyber-social Learning Laboratory

Mission

To design and research learning environments that establish a dialogue between artificial and human intelligence.

Precepts

Cyber-social learning connects people (who give meaning to things) with computers (which quantify and calculate these things). Such learning strives to be:

  1. Multilinear: traversing social networks and knowledge graphs (contrasted with linear, transmission models of standardized knowledge: teacher/text > student > one-shot test; and lock-step models knowledge progression)
  2. Collaborative: leveraging the social distribution of meanings, in legacy knowledge architectures/ontologies and group knowledge work (contrasted with individualized, “mentalist” pedagogies)
  3. Transpositional: creating a lively traffic between the sensorimotor and the cognitive, the world and its conceptual representation, human intelligence and computer intelligence; the ideal and the material (contrasted with the cognitivist biases of education, assessment, and some strands of artificial intelligence)
  4. Reflexive: offering small, rapid cycles of actionable human-human and human machine feedback (contrasted with long cycles, practically non-actionable cycles of traditional assessment).
  5. Non-scalar: nesting small scale e.g. peer-peer, one-to-one instruction, within an indefinitely scalable learning ecology (contrasted with the fixed scale of physical learning architectures and educational labor processes).

Experimental Technologies

  • CGScholar: Community (social-dialogical)
  • CGScholar: Analytics (educational data mining)
  • CyberScholar: AI supported writing space

Publications

  • Cope, Bill and Mary Kalantzis, "On Cyber-Social Learning: A Critique of Artificial Intelligence in Education,” pp.3-34 in Trust and Inclusion in AI-Mediated Education: Where Human Learning Meets Learning Machines, edited by Theodora Kourkoulou, Anastasia O. Tzirides, Bill Cope and Mary Kalantzis, Cham CH: Springer, 2024, doi: https://doi.org/10.1007/978-3-031-64487-0_1.
  • Tzirides, Anastasia O., Akash Saini, Gabriela Zapata, Duane Searsmith, Bill Cope, Mary Kalantzis, Vania Carvalho de Castro, Theodora Kourkoulou, John Jones, Rodrigo Abrantes da Silva, Jen Whiting and Nikoleta Polyxeni Kastania, "Generative AI in Education: Reflections from Application with Student Work,” pp.287-302 in Trust and Inclusion in AI-Mediated Education: Where Human Learning Meets Learning Machines, edited by Theodora Kourkoulou, Anastasia O. Tzirides, Bill Cope and Mary Kalantzis, Cham CH: Springer, 2024, doi: https://doi.org/10.1007/978-3-031-64487-0_13.
  • Cope, Bill and Mary Kalantzis, "Artificial Intelligence in the Long View: From Mechanical Intelligence to Cyber-social Systems,” Discover Artificial Intelligence, 2(13):1-18, 2022, doi: https://doi.org/10.1007/s44163-022-00029-1
  • Cope, Bill and Mary Kalantzis. 2022. "The Cybernetics of Learning." Educational Philosophy and Theory:1-37. doi: https://doi.org/10.1080/001318....
  • Cope, Bill, Mary Kalantzis and Duane Searsmith. 2021. "Artificial Intelligence for Education: Knowledge and Its Assessment in Ai-Enabled Learning Ecologies." Educational Philosophy and Theory 53(12):1229-45. doi: http://doi.org/10.1080/00131857.2020.1728732
  • Cope, Bill and Mary Kalantzis. 2019. "Education 2.0: Artificial Intelligence and the End of the Test."Beijing International Review of Education 1:528-43. doi: https://doi.org/10.1163/25902539-00102009.