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)

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