How 1970s Cybernetics Could Fix AI in the Classroom Today

Artificial intelligence keeps arriving in classrooms as a shiny tool handed down from above, and it keeps disappointing the teachers who receive it. A new synthesis published in Frontiers of Digital Education argues that the problem is not the technology itself but the mental model educators use to deploy it. Shantanu Tilak of the Center for Educational Research and Technological Innovation at Chesapeake Bay Academy in Virginia Beach proposes that a half-century-old framework, British cybernetician Gordon Pask’s conversation theory, offers a practical blueprint for weaving AI into collaborative learning rather than bolting it on. Drawing on four studies conducted with teachers, high school students, and college learners aged 18 and up at a special education independent school and an affiliated liberal arts university, the paper shows how AI can act as one participant in an emergent network of minds rather than a replacement for them.

Pask’s cybernetics, developed in the 1970s through works such as his 1975 volume on conversation, cognition and learning, treats learning not as the transfer of information from an expert to a novice but as a continuous loop of conversations between participants who must each build and test their own understanding of what the other knows. In this view, a classroom is a self-organizing system of interacting actors, and knowledge emerges from the negotiations among them. When an AI system enters the room, it does not merely deliver content; it becomes a node in that network, sending and receiving feedback, reshaping the conversations around it even as those conversations reshape how it is used. Tilak uses this lens to visualize classroom interactions across all four studies, mapping how human and machine agents circulate questions, answers, and corrections in cycles that never fully settle.

The first study examined teachers using MagicSchool AI, a generative platform aimed at educators, to design course blueprints. Rather than treating the AI as an automatic lesson generator, the participating teachers engaged in an iterative dialogue with it, feeding it their goals, critiquing its drafts, and revising prompts in response to what the system produced. The cybernetic reading of this process emphasizes that the value came from the feedback loop itself: the AI’s outputs forced teachers to articulate their pedagogical intentions more precisely, and those articulations in turn steered subsequent generations toward usable designs. The study, published in the Journal of Sociocybernetics in 2024, framed this as a participatory qualitative analysis, with teachers acting as co-designers of the curriculum rather than passive consumers of machine suggestions.

The second study moved from planning to production, asking college psychology students to use text-to-image generative AI to create comic strip storyboards for their coursework. Storyboarding has a long pedigree as a classroom technique for breaking down complex narratives into manageable visual sequences, and generative image tools gave students a new collaborator in that task. Students had to translate abstract psychological concepts into concrete scene descriptions, evaluate the sometimes uncanny outputs, and negotiate revisions. The synthesis highlights how this workflow distributed cognition across human and machine: the AI contributed visual fluency while the students contributed conceptual grounding, and the comic strips that emerged would have been impossible for either party alone. The findings, also published in the Journal of Sociocybernetics, suggest that creative assignments can channel generative AI away from plagiarism concerns and toward genuine collaborative problem-solving.

The third and perhaps most provocative study cast AI as a Socratic opponent. In a college psychology course, students engaged in interactive discussions where an AI was positioned not as an answer machine but as an interlocutor that challenged their reasoning, much as the classical Socratic method demands. Tilak and colleagues then applied network analysis, a statistical technique for mapping relationships among actors, to the resulting transcripts, revealing patterns in how discourse flowed among students and the machine. Published in 2025 in the Journal of Sociocybernetics, the comparative analysis showed that when the AI’s role was explicitly framed as an opponent rather than an oracle, students treated its claims more critically and defended or revised their own positions more actively. This connects to a growing literature on conversational agents in group settings, including research on how such agents should communicate within group chats, and to recent comparative work suggesting that AI tutors can, under the right conditions, support critical thinking alongside human instructors.

The fourth study addressed an equity concern that most AI-in-education debates overlook: students with disabilities. Working with high schoolers with ADHD in mathematics classes, Tilak and team member Marzena Bogacki built a cybernetic feedback mechanism using ALEKS, the Assessment and Learning in Knowledge Spaces platform. ALEKS models a learner’s knowledge as a network of concepts and continuously probes which items the learner is ready to acquire next. The research reframed this adaptive engine as a feedback controller in Pask’s sense: assessment data cycles back into instruction in real time, allowing the system, the teacher, and the student to adjust one another’s behavior. For learners whose attention and executive function make conventional pacing difficult, this tight loop of observation and adaptation offered a structure that static worksheets could not, though the study also underscored the importance of keeping a human teacher in the loop to interpret the data and respond to needs the algorithm cannot see.

Taken together, the four scenarios illustrate five of the six configurations of collaborative AI use originally outlined by learning scientist Mike Sharples in his 2023 work on social generative AI for education. Sharples has argued that educational AI should move beyond the tutor-as-responder model toward a vision in which AI systems participate in the social construction of knowledge, conversing with students and with each other. Tilak’s synthesis demonstrates what those abstract configurations look like on the ground: AI as a design partner for teachers, as a co-creative medium for students, as a dialectical adversary in discussion, and as an adaptive monitor in special education. Each configuration preserves the human relationships at the center of the classroom while extending what those relationships can accomplish.

The paper is equally candid about the grey areas that experts in AI education research have carved out, including unresolved questions about design and implementation, classroom relationships, and assessment. Ethical debates over trustworthy and educational AI are accelerating globally, and a 2024 consensus statement in the Proceedings of the National Academy of Sciences warned of threats to scientific integrity in the age of generative AI. Tilak’s response is not to retreat from the technology but to give educators a structured way to think about where responsibility lies in a hybrid system. If learning is a conversation, then accountability is also distributed: teachers must interrogate what the AI produces, students must own their interpretive contributions, and developers must build systems transparent enough to participate honestly in those exchanges. The synthesis argues that this distribution only works when it is designed deliberately, not left to emerge by accident.

The practical payoff of the research program is a set of shepherding suggestions for three core activities: AI-mediated curriculum design, classroom problem-solving and information acquisition, and nimble student evaluation. For design, the recommendation is to treat prompt engineering as an extended conversation about pedagogy rather than a one-shot command. For classroom work, the recommendation is to assign AI roles, opponent, collaborator, or monitor, explicitly, so students understand what kind of intelligence they are engaging and calibrate their trust accordingly. For assessment, the recommendation is to use learning data as part of a feedback loop that informs the next teaching move, rather than as a terminal judgment. The author, whose broader program also includes experimental work on sociohistorically sensitive information search tools and conceptual studies linking cybernetics to psychological theory, notes that the sixth of Sharples’s collaborative configurations remains unexplored, and that a planned series of subject-specific scenarios using AI as a collaborative coach is still ahead.

What makes this research resonate beyond its immediate findings is its refusal of the two tired scripts that dominate public discussion of educational AI: utopian salvation and inevitable ruin. Pask’s cybernetics, precisely because it predates the current hype cycle, offers a vocabulary for something in between, a view of classrooms as living systems in which new actors can be integrated without displacing the old ones. As schools worldwide race to draft AI policies with little empirical grounding, the four studies offer evidence that the deciding variable is not the sophistication of the model but the quality of the conversational architecture built around it. In that sense, the most futuristic guidance for AI in education may come from a theorist who never saw a chatbot: design the conversation first, and the technology will find its place within it.

Subject of Research: AI-mediated collaborative learning guided by cybernetic conversation theory in adult and special education classrooms

Article Title: A Cybernetic Guide to Implementing AI for Collaborative Learning: A Synthesis of Four Studies Conducted with Adult Learners

Article References: Tilak, S. (2025). A Cybernetic Guide to Implementing AI for Collaborative Learning: A Synthesis of Four Studies Conducted with Adult Learners. Frontiers of Digital Education, 2(4), Article 37. https://doi.org/10.1007/s44366-025-0074-7

Image Credits: AI Generated

DOI: 10.1007/s44366-025-0074-7

Keywords: artificial intelligence, cybernetics, Gordon Pask, collaborative learning, generative AI, education, special education, conversation theory, Mike Sharples, Socratic method, adaptive learning, classroom design