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# System prepended metadata

title: 'Data Science Education Interest Group: AI in Education'

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Data Science Education Interest Group: AI in Education
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| Agenda                   | Speaker                    |   Time      | 
| ------------------------ | ------------------------------- | ----------------- |
| Intro and overview       | Ayesha             | 13:00 - 13:05   | Complete
|  AI in education: A brief history with examples (beyond the hype)  | Professor Manolis Mavrikis | 13:05 - 13:25  | Complete
|  Mitigating bias             |   Jamiu Idowu   |  13:25 - 13:35  | 
| Other ethical and societal implications              |   Bernice Yeo and Sopio Zhgenti   |  13:35 - 13:40  | 
| Q&A and breakout discussions             |   All   |  13:40 - 13:55  | 
| Wrap-up | Matt Forshaw        |    | Complete |

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### Breakout discussion questions

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#### 1. Raising understanding and awareness: what can be done to further this?

- Nancy R - Infuse AI concepts across curriculum for awarness - alert students to use of AI practices within the educational domain
- Syaamantak Das - Bring in two aspects of AI - No Code AI for getting hands on experinces and Conversational AI as a guide/help during the process of learning.
- Martin G - more training. A lot of people seem to just avoid using AI in education completely as they're scared of it or think students will just use it to cheat. So just more of the same, teaching people to effectively use AI to enhance education, particularly focussing on how it could benefit educators as well as students.
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#### 2. What is missing in the field of AI in Education?

- Nancy R - an understanding of the ways in which AI is used to support, track, assess and predict student success
- Syaamantak Das - Whether AI can predict any potential conceptual roadblock in the learning process by analyzing the curriculum content and interactions made by the students
- Martin G - general/high level understanding of how these systems work (where appropriate). XAI in Education.
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#### 3. What can be done (by institutes/individuals/policymakers) to mitigate issues?

- Nancy R - Have more transparency
- Syaamantak Das - Improvise on existing systems by including No Code AI / Conversational AI as interventions.
- Martin G - make it clear to students how these systems can be used. Also ensuring AI isn't being used to replace understanding. For example, when writing code with Copilot students should understand how to program on their own first so they can fix problems when things go wrong. In addition, when different solutions are offered or the problems are too complex to be solved by Copilot. 
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#### 4. Please share your details if you wish to collaborate or join a working group on the above.

- Nancy R
- Tomasz K
- Syaamantak Das (syaamantak-das.carrd.co)
- Eliana (eliana.osoriosaez@bristol.ac.uk)
- Martin Goodfellow (martin.h.goodfellow@strath.ac.uk)
- Bobbi Moore (b.j.moore@soton.ac.uk)
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