Positive Computing: A Human-Centred Approach to Technology in Education

As in every generation before ours, the discourse around education is increasingly one of fear and despair. PISA results are declining, student depression is rising, and AI is replacing our teachers. It is easy to see why people are losing heart. But this is not, I believe, an inevitable outcome. In this three-part series I offer a counter-narrative, one drawing on the voices of learners, teachers, and communities. One that is proactive instead of reactive. And one where technology’s affordances are redirected towards one single purpose: enhancing human wellbeing.

I will offer this narrative in three parts. In the first, I will propose a realignment in how we relate to technology. I will use the lens of hybridity, or the understanding that our biological realities are increasingly entangled with our digital ones. Rather than seeing this as a necessary loss of agency, I suggest that the ability to orchestrate this relationship gives us an opportunity to subjugate technology to our will. In the second part, I will explore the concept of heutagogy, or self-determined learning. I will argue that the ability to establish, pursue and reflect on our own aspirations is the ultimate manifestation of our ability to shape our future. In the third part, I will explore humanisation, or the centering of human flourishing as the primary aim of education. I will argue that positive computing and other similar approaches can help us to achieve this goal.

Part 1: Navigating Hybridity

The end of independent thinking?

We tend to think of thinking as something that happens inside our heads. Certainly, when we assess students, we generally expect them to demonstrate what they know and can do independently. Other people, books and technologies may help them prepare, but the final achievement is presumed to belong to the individual.

Yet human cognition has never been entirely independent. From the tools we use to support our thinking (books, calculators), to our shared cultural narratives (the children’s stories we grew up with), we have always engaged in distributed cognition (Hutchins, 1995). What is changing is the nature of the interactions we engage in. Generative AI can participate in activities we previously regarded as distinctly human: developing arguments, evaluating alternatives, generating explanations and producing creative work. This raises an intriguing question: when we think with AI, whose thinking is it?

Living in a postdigital world

The concept of postdigitalism offers a useful starting point. It recognises that digital technologies are so thoroughly embedded in our social, cultural and cognitive lives that distinguishing between digital and non-digital experience is becoming increasingly artificial (Jandrić et al., 2018).

Consider how the boundaries of learning have changed. A student might encounter an idea in class, discuss it with friends, explore it through an AI conversation, test it during a workplace project and share the results with an international online community. Formal and informal learning, physical and digital spaces, and individual and collective knowledge become intertwined.

Similarly, mobile and wearable technologies extend our capacities to perceive, remember, communicate and navigate. We routinely rely on digital systems to perform activities that previously required considerable individual knowledge or effort.

These developments represent more than technological convenience. They invite us to reconsider the boundaries between ourselves and the technologies through which we increasingly experience the world.

Thinking with AI

Generative AI adds a particularly interesting dimension to this hybridity. Unlike a calculator, which performs a relatively circumscribed function, AI can participate in extended exchanges in which ideas are progressively developed, challenged and refined.

Consider a student preparing an argument about climate policy. She might begin by asking AI to identify competing perspectives, then challenge its assumptions, request counterarguments, compare evidence and progressively develop her own position. Neither her initial ideas nor the AI's first responses fully determine the outcome. New possibilities emerge through their interaction.

Research on learners' experiences with generative AI suggests that such relationships can involve ongoing negotiation rather than straightforward delegation. Alm (2024), for example, describes language learners developing different ways of working with AI according to their individual goals, with agency emerging through the interaction itself.

This is what makes hybrid cognition interesting. Human and technological capabilities can complement one another, potentially making forms of inquiry and creativity possible that would otherwise be difficult to achieve.

Augmentation or Outsourcing?

Imagine two students submitting equally impressive assignments. One has used AI to generate most of the content, making minor revisions before submission. The other has used it to explore different perspectives, identify weaknesses in her reasoning and develop a more sophisticated argument. Their final products might be indistinguishable, but their learning experiences are clearly not.

This distinction matters because educational technology is frequently evaluated through improvements in performance, efficiency or output quality (in particular by the companies creating the technology). Yet an impressive result does not necessarily indicate that the learner has developed greater understanding or capability.

A similar paradox arises in personalised learning. An adaptive system might efficiently select exercises, adjust difficulty and recommend learning pathways. But if these decisions are made entirely by algorithms, cognitive efficiency may trump all other learning outcomes (e.g. critical thinking, socio-emotional regulation, teamwork), learners end up with a hyper-personalised diet that leaves them working alone (what Sherry Turkle refers to as being ‘alone together’; 2011) and with little opportunity to develop the judgement needed to direct their own learning. In this case, the technology may be adapting to the learner without the learner becoming more capable.

This is the fundamental tension of hybrid cognition: technology can augment our thinking, but it can also encourage us to relinquish, or outsource, the very cognitive processes through which we develop.

The distinction is not simply between using and avoiding AI. It concerns how human and technological contributions are configured, and whether that relationship develops or diminishes human capability.

Towards positive computing

This brings us to positive computing: the design and use of technologies that explicitly support human wellbeing and flourishing (Calvo & Peters, 2014).

Rather than assuming that technological advancement necessarily constitutes human progress, positive computing begins with a different question: How can we shape our relationships with technology to become more capable, connected and fulfilled?

For hybrid cognition, this means looking beyond what human-AI partnerships produce to consider what participating in them does for the human. An AI system that simply supplies answers may save time. One that challenges our assumptions, prompts reflection, encourages curiosity or helps us recognise gaps in our understanding may contribute something more enduring.

The objective is not to insist that humans perform every cognitive task independently. Nor is it to maximise the amount of thinking delegated to machines. It is to develop thoughtful relationships in which technological capabilities expand our opportunities for meaningful intellectual and personal development. This requires us to remain critically engaged with technology, including questioning its recommendations, recognising its limitations and deciding when its assistance serves our purposes.

The future of thinking may well be hybrid. Whether that makes us more capable, however, is not a technological question alone. It depends on the relationships and ways-of-being (phronesis) we choose to cultivate.

In the next article, I will explore how heutagogy, or self-determined learning, can help us develop the agency to make those choices.

References

• Alm, A. (2024). Exploring autonomy in the AI wilderness: Learner challenges and choices. Education Sciences, 14(12), 1369. https://doi.org/10.3390/educsci14121369
• Calvo, R. A., & Peters, D. (2014). Positive computing: Technology for wellbeing and human potential. MIT Press.
• Hutchins, E. (1995). Cognition in the wild. MIT Press.
• Jandrić, P., Knox, J., Besley, T., Ryberg, T., Suoranta, J., & Hayes, S. (2018). Postdigital science and education. Educational Philosophy and Theory, 50(10), 893–899. https://doi.org/10.1080/00131857.2018.1454000
• Turkle, S. (2011). Alone together: Why we expect more from technology and less from each other. Basic Books.