Making sense of arguments about AI in the future of education

Forward-Woman-Artificial-Intelligence-Robot-507811_400x283I’m attempting to develop some kind of framework to make sense of the emerging wealth of ideas about artificial intelligence (AI) and it’s potential impact on teaching and learning. This is a first very tentative step in trying to give shape to scaffold our thinking about how AI might change our roles as teachers, our relationships with students, what and how we teach and perhaps what we should be hoping for in an AI rich future.

While I’m still scratching the surface, it seems to me that the arguments about AI in education I’ve explored so far can be categorised against three types (partly inspired by Simon Sinek’s “Why-How-What” Golden Circle)…

(Why) – Learning because of AI

That students will need different knowledge and cognitive skills to survive and thrive in the face of the economic, social, political and cultural disruption that AI will trigger. Non-intelligent digital technology has already taken over many mundane and repetitive tasks of life and work and AI threatens to take over middle-order thinking and procedural processes as well. This includes the “robots are coming for your jobs” arguments. In such a world human value-add is expected to be concentrated in areas such as higher order thinking, collaboration, creativity and judgement.

(How) – Learning through AI

That AI systems and agents will offer new opportunities and challenges for the way teaching and learning occurs. Educational software and eLearning environments powered by AI are expected to provide for personalisation, targeted instruction and timely feedback for learners, enabling transformation of learning processes, experiences, spaces and systems. This includes the “your role as a teacher will be transformed” arguments.

(What) – Learning about AI

That students will need skills and knowledge to allow them to deal with AI in their future lives. They will need to know how to code, to design, to configure, to direct – not just interact with and react to – AI systems and agents, to ensure they are empowered to lead satisfying and successful lives in an AI-rich future. This includes the “we need to teach students to be creators of technology, not just consumers” arguments.

There are also some economic and political arguments/discussions that contribute to the broader context within which education change can be visioned. These include equity and wealth distribution, environmental impacts/considerations, cultural change and the future of work, industry and remuneration.

What do you think? All comments are welcome. My next ambition is to map this Why-How-What organiser against another educational frame (e.g. Bloom’s Taxonomy, PBL Essential Design Elements, TPACK, SAMR etc) to see if the intersections can offer richer ideas and a model to support decision making.

(Also published on the Learning Place 11 April 2017)


Brunskill, Emma (2017) Playtime’s Over: Getting computers to beat humans at games is impressive. But now the real work begins; MIT Technology Review,, last viewed 11 April 2017

Dickson, Ben (2017) How Artificial Intelligence Enhances Education; The Next Web,, last viewed 11 April 2017.

Dickson, Ben (2016) Robots are taking all of our jobs. What’s next? Tech Talks,, last viewed 11 April 2017.

Drabkin, Ron (2017) Machine Learning: The “Next Big Thing” in Education, Getting Smart, last viewed 11 April 2017.

Luckin, Rose and Griffith, Mark (2016) Intelligence Unleashed: An argument for AI in Education; Pearson,, last viewed 11 April 2017.

Online Universities (2012) 10 Ways Artificial Intelligence Can Reinvent Education; Blog,, last viewed 11 April 2017.

Vander Ark, K and Vander Ark T (2017) Rise of AI Demands Project Based Learning; Getting Smart,, last viewed 11 April 2017.

Wood, Alex (2016) Artificial intelligence is the next giant leap in education; Raconteur,, last viewed 11 April 2017.

Yao, Mariya (2017) Why We Need To Democratize Artificial Intelligence Education; Forbes,, last viewed 11 April 2017.

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