Artificial Intelligence in education and Bloom’s Taxonomy

bloomA little while ago I tried to make some sense of the various arguments about the impact of artificial intelligence (AI) in education. I suggested they could be categorised using a Why-How-What model.

The idea was to create a basic platform for looking at issues relating to AI and education futures through the lens of well-established education frameworks.

My first attempt has been to use the Why-How-What model to look at AI in education through the lens of Bloom’s taxonomy.

The first thing I noticed was how hard it was to capture all the possible predictions, issues, hopes, fears and thoughts that surround the prospect of an AI rich education future. So I didn’t try. Instead I settled for some illustrative examples that might be useful for generating discussion.

I also found that each column was populated by distinctly different ideas which suggests the Why-How-What model may be useful in trying to make sense of the complexity of this topic.

Why – Learning because of AI
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.

This column was populated with examples of how different levels of cognition might be performed by AI in our futures. I found myself thinking about the future purpose of education especially as it relates to the kinds of cognition AI and humans will increasingly do.

How – Learning through AI
AI systems and agents will offer new opportunities and challenges for the way teaching and learning occurs.

These ideas explore how AI might influence the way teaching and learning occurs. I imagined a cognitive scaffold being implemented in a future classroom supported by AI agents and systems, and wondering what would change in a teacher’s role in a learning environment .

What – Learning about AI
That students will need skills and knowledge to allow them to deal with AI in their future lives.

This column contains examples of learning goals that could be important in preparing students for an AI rich world and how they might be situated at different levels of cognitive activity.

Categories of AI-in-education arguments Vs Bloom’s Taxonomy
  Why How What
Learning because of AI Learning through AI Learning about AI
Remember
Recall facts and basic concepts
AI systems such as personal assistants give us rapid access to access facts and concepts when needed particularly those that are specialised, detailed and domain-specific. There will still be core facts/concepts required to support higher order thinking more generally (e.g. literacy, numeracy, information literacy) AI systems provide students with personalised instruction in factual information at the time and pace they need. Teachers and curriculum designers determine the essential information students need to learn. Students learn basic concepts that feature in the world of AI (e.g. algorithms) and can define them.
Understand
Explain ideas or concepts
AI systems can develop and reuse understanding of concepts through processes such as machine learning. The role of humans may evolve to assisting AI to refine understanding within a real world and human context (e.g. policy, culture, philosophy). AI tutors provide students with customised learning pathways based on demonstrated prior understanding, interest and need. Teachers configure and/or monitor the performance of both students and AI tutors. Students develop a basic personal understanding of AI or particular AI forms/concepts (e.g. machine learning).
Apply
Use information in new situations
AI systems can apply information to new situations through their ability to learn from experience, especially in well-defined existing activities. Human application of knowledge may still be needed for complex and new situations. Teachers design learning experiences that require students to apply their knowledge to new situations and provide access to AI to support students through timely feedback and instructional support. Students explore an everyday problem/activity and identify what aspects could be solved through AI.
Analyse
Draw connections among ideas
AI can perform a range of analytic tasks and leverage experience to refine these for different contexts. Human-AI partnerships would allow these to be priotised and preferenced for human needs. Teachers design learning experiences that require students to analyse information and provide access to AI to support students by scaffolding complexity, generating scenarios and provding feedback. Students create a concept map to sort a number of everyday problems/activities into groups according to their suitability for different AI applications
Evaluate
Justify a stand or decision
Judgement may remain a significant value that humans can perform better than AI, particularly in the context of policy, philosophy, values and culture. Teachers and students design learning experiences and missions that require evaluation of ideas and solutions. AI supports student learning at lower levels of the cognitive scaffold. Students develop arguments about the role and impact of AI in their future.
Create
Produce new or original work
The design and construction aspects of creating may be/become well supported by AI but innovation and creativity should remain a competitive advantage for humans. Teachers and students design learning experiences and missions that require design and creation of innovative solutions. AI supports student learning at lower levels of the cognitive scaffold. Students create artefacts to imagine their future in an AI world (e.g. personal timeline, infographic, animated short film).

(Also published on the Learning Pace 20 April 2017)

AI and personalised learning – show us the ways

Physics-Education-Student-Mathematics-School-Learn-1996846One of the most popular “How” arguments in artificial intelligence for education relates to personalised learning. Essentially the argument goes…

“AI will allow us to personalise learning for individual students and get away from the one-size-fits-all approach”

But what will this look like? What’s already happening and is it working?

Microsoft founder Bill Gates and Facebook CEO Mark Zuckerberg are both strong proponents of the idea that AI can personalise learning. Both point to a number of existing and emerging technologies that they say promise to improve outcomes for students by offering them experiences and pathways that cater to their specific needs. Both are investing through their respective foundations in efforts to see personalised learning solutions developed for schools.

Learning and class management systems

Proponents of AI for personalised learning often point to online systems like Blackboard, Google Classroom and Edmodo that allow teachers to collect, curate, combine and communicate content to students in ways that are customised for individuals and groups. This allows teachers to break the traditional uniform delivery mode of traditional teaching and help students work on the specific concepts they need and/or are ready to engage.

It is debatable whether this really involves or requires artificial intelligence though – this kind of functionality has existed for at least 20 years and the personalisation is really coming from the teacher unless one of the other concepts (e.g. below) is also in play.

The Learning Place offers this functionality through edStudiosLearning Pathways and Virtual Classrooms.

Adaptive learning

This is where systems use algorithms to automate the collation of learning experiences for students based on their demonstrated learning needs. In the Australian assessment system Improve, for example, students can undertake a teacher-created test and then receive learning activities the system determines will help them improve in areas of underperformance.

It has been argued that software could be developed that also takes into account students’ individual learning styles – notwithstanding the doubt over their existence.

Standards-enabled learning

One of the propositions of AI in education is that software could assist teachers and students with the often laborious and complex task that involves mapping learning experiences to educational standards such as Common Core (USA) or the Australian Curriculum. In the Learning Place and Scootle, algorithms already help teachers match curriculum outcomes with relevant learning resources (and back again) and more intelligent systems could conceivably analyse more complex and authentic learning experiences (e.g. project based learning) to map to standards.

Personalised AI Tutors

A seemingly far-fetched idea is that an AI enabled tutor could be used to offer advice and feedback to learners, not just for curriculum areas that involve right-wrong answers but in subjects that normally involve higher-level conceptual skills and understanding such as writing. AI is already used in journalism to create news reports that have been shown to be hard to distinguished from human-generated ones, so is it too hard to imagine software that can analyse a student’s writing and offer feedback? This argument also points to the value for learners who might be reluctant to seek feedback from peers or a human teacher although some thought about what a teacher’s role would be, and how the teacher-student relationship would/should evolve, is probably needed.

Reporting, accountability and home-school engagement

If curriculum planning and delivery can be personalised through AI, then it is arguable that assessment and reporting can also be individualised with much of the process automated to reduce rather than increase workload on teachers. However, it will probably require a mindset change away from standards as a tool for ranking teachers and schools, to the use of standards and AI as a way of creating more effective learning partnerships between schools, their students and families.

Image credit

CC0 http://maxpixel.freegreatpicture.com/Physics-Education-Student-Mathematics-School-Learn-1996846

(Also published on the Learning Place 18 April 2017)

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)

References

Brunskill, Emma (2017) Playtime’s Over: Getting computers to beat humans at games is impressive. But now the real work begins; MIT Technology Review, https://www.technologyreview.com/s/603504/playtimes-over/, last viewed 11 April 2017

Dickson, Ben (2017) How Artificial Intelligence Enhances Education; The Next Web, https://thenextweb.com/artificial-intelligence/2017/03/13/how-artificial-intelligence-enhances-education/#.tnw_qfHD822u, last viewed 11 April 2017.

Dickson, Ben (2016) Robots are taking all of our jobs. What’s next? Tech Talks, https://bdtechtalks.com/2016/06/13/robots-are-taking-all-of-our-jobs-whats-next/, last viewed 11 April 2017.

Drabkin, Ron (2017) Machine Learning: The “Next Big Thing” in Education, Getting Smarthttp://www.gettingsmart.com/2017/04/next-big-thing-education/, last viewed 11 April 2017.

Luckin, Rose and Griffith, Mark (2016) Intelligence Unleashed: An argument for AI in Education; Pearson, https://www.pearson.com/content/dam/corporate/global/pearson-dot-com/files/innovation/Intelligence-Unleashed-Publication.pdf, last viewed 11 April 2017.

Online Universities (2012) 10 Ways Artificial Intelligence Can Reinvent Education; OnlineUniversities.com Blog, http://www.onlineuniversities.com/blog/2012/10/10-ways-artificial-intelligence-can-reinvent-education/, last viewed 11 April 2017.

Vander Ark, K and Vander Ark T (2017) Rise of AI Demands Project Based Learning; Getting Smart, http://www.gettingsmart.com/2017/03/rise-of-ai-demands-project-based-learning/, last viewed 11 April 2017.

Wood, Alex (2016) Artificial intelligence is the next giant leap in education; Raconteur, https://www.raconteur.net/technology/artificial-intelligence-is-the-next-giant-leap-in-education, last viewed 11 April 2017.

Yao, Mariya (2017) Why We Need To Democratize Artificial Intelligence Education; Forbes, https://www.forbes.com/sites/mariyayao/2017/04/10/why-we-need-to-democratize-ai-machine-learning-education/#4d49c3f01197, last viewed 11 April 2017.

Image credit

CC0 http://maxpixel.freegreatpicture.com/Forward-Woman-Artificial-Intelligence-Robot-507811

AI and the future of the classroom

256px-HAL9000An interesting article from Rose Luckin and Wayne Holmes on How We Get to Next, imagines what a classroom would look like in ten years time when robotic teaching assistants run on artificial intelligence, work alongside teachers to deliver personalised and highly targeted learning to digitally connected students.

A.I. is the New T.A. in the Classroom imagines a Year 4 classroom where a digital construct (“Colin”) works not as a new educational overlord but as a dedicated and benevolent assistant to the teacher (“Jude”) who in turn enjoys a significantly more insightful view of her students’ needs.

An interesting read which prompts a few immediate questions for me:

  • What access will different school communities have to A.I. resources?
  • Who will program A.I. services and how might this effect the way their role evolves? (Note this article is published “in association with Pearson”)
  • Given A.I. is based on deep learning, what happens as the “Colins” of our classrooms become rapidly more adept?
  • With all the power of A.I. why do we still imagine the majority of learning would still occur in physical classrooms like those we have today?
  • What are the potentials/implications if learning extends beyond the physical and organisational structures of our current school systems?

(Also published in the Learning Place 4 April 2017)