Democratic solutions for AI-enhanced Personalised education

1. Democratic solutions for AI-enhanced Personalised education Sandra Leaton Gray and Natalia Kucirkova

2. Talk outline • 1, Personalised education • 2, Personalised learning • 3, AI in education • 4, Assessments • 5, Personalised assessments • 6, AI-enhanced personalised assessments • 7, Democratic classrooms • 8, AI-enhanced personalised democratic education

3. Personalised education ≠ standardised education • -a poorly defined concept with a significant potential to transform public education -our focus is on personalised data-based education = personalised learning and teaching informed by data, such as students’ individual assessment and performance scores  focus needs to be on an individual student to count as “personalised” and not “customised” or “universal”

4. Personalised or standardised?! • technology-driven, externally imposed commercial models of personalised education are not personalised • Paradoxically, they standardise students’ learning according to a particular Western, commercialised model of education. • They do this through an increased technological monopoly, clear commodification of knowledge, and a marketised approach to children’s education • For full debate, check: • Kucirkova, N. (2018) Is Silicon Valley Standardizing 'Personalized' Learning?, Education Week, • https://www.edweek.org/ew/articles/2018/05/30/is-silicon-valley-standardizing-personalized-learning.html

5. Personalised learning • Kucirkova studied personalised learning in the context of children’s reading of print/physical books and e-books/apps • There are pros: • Children are more motivated to read personalised books • Children learn more new words in personalised versus non-personalised condition • Digital personalised books created by users support positive atmosphere around technologies at home • There are cons: • Increased self-focus documented through children’s spontaneous speech • Non-agentic choices imposed on students and teachers restrict classroom conversations around books

6. References • Kucirkova, N. & Cremin, T. (2017) Personalised reading for pleasure with digital libraries: Towards a pedagogy of practice and design, Cambridge Journal of Education, doi: http://dx.doi.org/10.1080/0305764X.2017.1375458 • Kucirkova, N., Messer, D., & Sheehy, K. (2014) Reading personalized books with preschool children enhances their word acquisition, First Language,34 (3), 227-243. • Kucirkova, N., Messer, D., Sheehy, K., & Fernandez-Panadero, C. (2014) Children's engagement with educational iPad apps: insights from a Spanish classroom, Computers & Education, 71, 175-184. • Kucirkova, N., Messer, D., & Sheehy, K. (2014) The effects of personalization on young children's spontaneous speech during shared book reading, Journal of Pragmatics, 71, 45–55, doi: http://dx.doi.org/10.1016/j.pragma.2014.07.007 • Kucirkova, N., Messer, D., Sheehy, K. and Flewitt, R. (2013) Sharing personalized stories on iPads: a close look at one parent-child interaction. Literacy, 47 (3), 115-122. • Contact: n.kucirkova@ucl.ac.uk

7. AI in education • =AI is a ‘feature, function or characteristic of computer systems or machines that try to simulate human-thinking behaviour or human intelligence’ (Kose, 2014, p.2) • - identified as one of the transforming forces of public education • AI practice and research in learning sciences are not aligned in current models • AI in personalised education reproduces historic and socio-economic biases • AI-enhanced personalised education currently means adaptive assessment. • We argue this reproduces educational inequalities. • We consider alternative models with a focus on assessment.

8. Assessment • Classic forms of assessment: • -discrete-points, multiple points and task-based tests • -essays, written and oral exams • More innovative forms of assessment: • -authentic forms of writing (e.g., the use of story boards, blogs, e-journals or Wiki pages); • -documentation of the learning experience and reflective process (e.g., use of a reflective journal, portfolio and annotated bibliography) ; • -adoption of alternative assessment methods (e.g., the production of exhibitions, leaflets and posters, videos and performances). • Personalised forms of assessment • Students choose their preferred form of assessment

9. Pros and cons of personalised assessments • Pros: • -If students choose their own method of assessment, they report greater satisfaction with the course (Garside, Nhemachena, Williams & Topping, 2009) • -Well-crated personalised data-based assessment methods could positively influence student motivation and inclusiveness of assessment methods (O’Neill, 2017) • Cons • -students’ choices are limited by their own familiarity with certain assessment methods • -students’ choices are limited by varied assessment workloads and development of different skills associated with different assessment methods (O’Neill, 2017) •  Personalised assessments do not achieve equity and inclusivity

10. AI-enhanced Personalised assessment • -AI could facilitate the choice of personalised assessments in well-defined learning situations with fixed outcomes • -AI could generate simple feedback AND teachers will need to provide elaborated feedback in order for it to be sufficiently useful • -Personalised data-based education enhanced with AI methods could significantly mitigate against bias risks and improve transparency of assessments • BUT: the wider issues impelled by AI-enhanced personalised education is the systems’ misalignment with democratic values in education. •  Let’s press the pause button and consider democracy in education

11. ‘A democratic classroom is a place where individuals act with a sensitivity to the needs of other community members, which involves managing the tension between individual needs and desires, and the requirements of the group.’

12. Bernstein’s conditions for democratic classrooms • In Pedagogy, Symbolic Control and Identity: Theory, Research, Critique (1996/2000) Bernstein lays out a framework of what he calls ‘conditions for democracy’ • Enhancement -seeing past and possible futures for pupils. • Inclusion -social, cultural, intellectual and personal inclusion operate individually (as well as groups). • Participation -right to participate in civic practice, through procedures whereby order is constructed, maintained and changed. •  To what extent are these three rights respected in the context of personalised learning and the artificial intelligence systems that underpin its provision?

13. Our suggested three principles • 1, The systems need to encourage the achievement of economic equality. Personalised data-based education needs to be implemented together with assessment systems that enable the growth of all children, not only those who have the access to or possess resources and knowledge. • 2, The design of personalised learning technologies needs to be more community-oriented, to ensure that personalisation does not happen at the expense of pluralisation (aka diversification). • 3, Personalised learning systems need to follow a more participatory approach towards its innovative outputs, in which children are positioned as makers and active citizens, and educators as those who determine content and its assessment.

14. Special Issue • London Review of Education • Artificial intelligence and the human in education • We invite papers that discuss, analyse and evaluate the ways in which AI-enhanced education could encourage human flourishing and reduce systematic biases in school access, assessment and achievement. In particular, we encourage authors to draw on their observations, interviews with practitioners and other members of public to reflect on issues of power in light of transnational elites, as well as social justice in light of state regulation and individual autonomy. Expressions of interest (via 200-word abstracts) are invited until 31st October 2018. Deadline for article submissions (6000-word-long conceptual and empirical articles) are accepted until 30th of March 2019.

15. Comments/Follow-ups… Thank you for your attention! • s.leaton-gray@ucl.ac.uk • n.kucirkova@ucl.ac.uk • @drleatongray • @Nkucirkova Slide design adapted from SlidesCarnival