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AI in Higher Education - Current situation: trying hard but must do better, #edlw2019

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Presentation by Tony Bates, EDEN Senior Fellow, at the 2019 European Distance Learning Week's fourth-day webinar on "Artificial Intelligence (AI) in Higher Education" - 14 November 2019
Recording of the discussion is available: https://eden-online.adobeconnect.com/p7d4zev81s1s/ & https://www.youtube.com/watch?v=4eebqKEIcM8

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AI in Higher Education - Current situation: trying hard but must do better, #edlw2019

  1. 1. AI in Higher Education Current situation: trying hard but must do better Tony Bates
  2. 2. Definition problems: • Statistical analysis (multiple regression; analysis of variance) – well established • Learning analytics: data ‘trawling’; search for patterns: no decision-making • AI: use of algorithms to identify (and interpret?) patterns of student behaviour: decision making
  3. 3. Decisions? Actions?
  4. 4. Core features Massive amounts of data Powerful algorithms for • search/identify • data analysis • pattern recognition Very powerful computing (cloud) Image: Datamation, 2019
  5. 5. Applications of AI in HE Institutional: • admissions/enrolment; marketing; curricular Student support • Intelligent tutoring e.g.(chatbots); automated feedback; early warning/at-risk Instructional • adaptive learning (test and re-direct) • assessment (automated grading)
  6. 6. Main impact to date on HE Very little: • Mainly institutional level (‘screening’) • Mainly ‘old’ AI (adaptive learning) • Some learning analytics: mainly NSD • Experiments with MOOCs • Limited to ‘low levels’ of learning (comprehension/understanding: declarative knowledge - NRC)
  7. 7. Why so little impact? Papers mainly by computer scientists Limited understanding of learning (behaviourist) Data rather than theory-driven Contrary to values of education • equity; • agency of the individual Data sets too small: HE fragmented
  8. 8. BUT: don’t be complacent Change will be driven by global internet platforms AI goal: replace not improve HE system: meet employer expectations; bypass the institutions; reduce costs; A potential commercial educational system based on AI
  9. 9. Questions Is AI an opportunity or a threat? Is it all smoke and mirrors? Will it disrupt the HE system? What are the implications of AI for: learners? teachers? institutions?