Generative AI is reshaping education into a shared space where humans and machines co-create intelligence. B. Mairéad Pratschke explores how educators can rethink learning design, embrace new AI-integrated pedagogies and develop intelligent communities, ensuring that AI tools become a collaborative partner in fostering understanding and critical thinking while aligned to educational goals.
In 1956, a group of academics at Dartmouth College coined the term “artificial intelligence”. Their mission was “to find out how to make machines that use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves”. This dual mission of creating intelligent machines that use natural human language is key to understanding the recent trajectory of generative AI and its impact in education.
Intelligence is historically the domain of formal education. Educators are the original knowledge workers: their product is academic research disseminated in teaching. The goal of creating intelligent machines that might rival humans in terms of their ability to construct, create and disseminate knowledge is therefore significant for its potential impact on education.
The prospect of a machine that can work alongside human experts at PhD level is clearly a future that educators need to take seriously.
While education is currently focused on the use of machine intelligence in the form of AI assistants, chatbots and tutors, in industry, the focus is on autonomous agents to scale service and operations and humanoid robots to perform formerly human labour. This increase in capabilities means that humans will increasingly interact with intelligent machines as presences in their personal and professional lives. Since the introduction of reasoning models in 2024, the ground has shifted again as generative AI capabilities now extend into the metacognitive (thinking about its own thinking) and agentic (acting autonomously without human direction) realms.
The prospect of a machine that can work alongside human experts at PhD level is clearly a future that educators need to take seriously.
Generative AI as a presence
Punya Mishra has argued that, for education, “it is more accurate to consider GenAI as creative, generative, reasoning, social ‘psychological others’ than tools that write papers and summarise information in an conversational tone”. More than that, these ‘psychological others’ appear in the form of presences – avatars, assistants, tutors, agents or humanoid robots – with which we interact using natural human language. This requires a revision of traditional approaches to technology integration in education.
In 2006, Mishra created the TPACK model to support the integration of digital technology into education. It was based on the interplay between content knowledge (CK), pedagogical knowledge (PK) and technological knowledge (TK). Since then, generative AI has radically changed the context in which education operates, which prompted Mishra to suggest adding contextual knowledge (XK) to the original TPACK framework, reflecting the dramatic change that generative AI has brought to the education landscape.
Content knowledge, as a result of collaboration with AI, becomes content intelligence.
Beyond recognising the dramatically changed context in which education exists, educators must also consider how our collaboration with AI as a form digital intelligence transforms each of the categories of knowledge outlined in the original TPACK framework. Content, pedagogy and technology knowledge are now better imagined as dynamic categories that reflect this emergent category of human and machine intelligence. Content knowledge, as a result of collaboration with AI, becomes content intelligence. Pedagogical knowledge for the AI age must be reframed as pedagogical intelligence. Technological knowledge becomes technological (or better, computational) intelligence. These changes reflect the dynamic interaction with a technology that is not static but rather developing all the time.
Replacing knowledge with a notion of a new hybrid form of intelligence that is both human and artificial, reflects the reality that knowledge is now constructed in collaboration with AI.
What this means for education
Collaboration with generative AI requires us to rethink how we design and deliver learning. Generative learning is an approach that characterises learning as “sense-making”, where learners link prior knowledge to new discoveries, and where educators design learning strategies to help learners construct knowledge and understanding. This idea of learning as sense-making is critical when using generative AI in education. It highlights the importance of fostering critical thinking and active interrogation over the passive acceptance of information.
Conversational AI clearly presents opportunities to utilise Socratic methods of learning through dialogue that pre-date our modern text-based approaches. Here the social interactions that come with generative AI are opportunities to expand the space for learning through dialogue using conversational AI. Diane Laurillard’s Conversational Framework offers a useful foundation from which to design AI-enabled dialogic learning based on active, generative principles. Here, the six active learning types (acquisition, discussion, collaboration, investigation, practice, production) serve as a starting point from which to design learning where students construct their own knowledge and understanding in dialogue with conversational and generative AI.
Educators can also design intelligent communities to increase access to expertise using generative AI. An intelligent community is made of humans collaborating alongside AI, with the latter’s role decided by the educator-designer. This idea of intelligent communities is based on the Community of Inquiry (CoI) framework, created to foster inquiry-based and collaborative online learning, which includes three forms of presence – social, cognitive and teaching presence – that make up the student experience. In this community, we can reframe social presence in the form of collaborator AIs, cognitive presence as analytical AIs and teaching presence as facilitator AIs.
Such intelligent, collaborative communities are already being used in advanced research. AI agents are filling the role of disciplinary experts on scientific research teams, bringing domain intelligence to interdisciplinary teams that would otherwise lack such expertise. In teaching and learning contexts, AI co-instructors increase student engagement while improving learning outcomes and encourage students to engage in critical thinking. Virtual peers provide 24/7 support for students, reducing anxiety during high-stress exam periods and improving exam results.
These and other early studies on the efficacy of AI for learning underline the importance of pedagogical alignment – that is, the design of AI tools to support critical inquiry and engagement, rather than taking shortcuts, and to ensure that using generative AI as a virtual presence supports rather than undermines learning.
Aligning goals and values
Generative AI has ushered in an era of human and machine intelligence that is poised to redefine how knowledge is created, constructed and disseminated. AI tools are now moving from knowing to doing, beyond simply generating content to co-creating and acting autonomously. As AI capabilities continue to improve, and they become more embedded in human activity, it is critical for human educators to be involved as active participants.
The future of education is undoubtedly hybrid, and creating human-centred AI requires AI-literate humans. Educators must learn to use generative AI, so that they can design tools that are pedagogically aligned to their goals and aligned with the interpersonal and emotional needs of students to protect user well-being. In doing so, they will be equipped to design and operate in intelligent communities of humans and AIs that are aligned to the values and goals of education.
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For educators keen to begin the journey outlined here, they can follow the five stages of the AI user maturity model, from prompting to creating intelligent communities of their own, in the book Generative AI and Education.
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