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AI applications in higher education - challenges and opportunities in ODE by Prof. Olaf Zawacki-Richter

EMPOWER: Artificial Intelligence in online education webinar week, day 3. Presentation by Prof. Olaf Zawacki-Richter

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AI applications in higher education - challenges and opportunities in ODE by Prof. Olaf Zawacki-Richter

  1. 1. AI applications in higher education – challenges and opportunities in ODE Prof. Olaf Zawacki-Richter University of Oldenburg, Germany EMPOWER Artificial Intelligence webinar week EADTU June 18, 2020
  2. 2. Folie 2 § What is AI and AI in Education? § What are potential areas of AI application in higher education? § How has research on AI in higher education developed over time? § What is the current state of AIEd? § What are opportunities, challenges and risks in ODE? Overview
  3. 3. Folie 3 https://en.wikipedia.org/wiki/Moore%27s_law (CC-BY-SA) Moore's Law BIG Data
  4. 4. Folie 4 § EDUCAUSE Horizon Report 2019 Higher Education Edition: Experts anticipate AI in education to grow by 48 anually until 2022 § Contact North (2018): "there is little doubt that the [AI] technology is inex- orably linked to the future of higher education" (p. 5) § Heavy investments: TU of Eindhoven will launch an AI Systems Institute with 50 new professorships for education and research in AI Relevance of AI in Education (AIEd)
  5. 5. Folie 5
  6. 6. Folie 6 § "the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly and learn from experience" (Gottfredson, 1997, p. 13) § John McCarthy (1956, cited in Russel & Norvig, 2010, p. 17): § The study [of artificial intelligence] is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. Intelligence is... Gottfredson, L. S. (1997). Mainstream science on intelligence. Intelligence, 24(1), 13–23. Russell, S. J., Norvig, P., & Davis, E. (2010). Artificial intelligence: A modern approach (3rd ed). Upper Saddle River: Prentice Hall.
  7. 7. Folie 7 § General Artificial Intelligence o aka "strong AI" o still only science fiction § Narrow Artificial Intelligence o aka "weak AI" o aka "good old-fashioned AI" (Haugeland, 1985) o aka "machine learning" o a one-trick horse Haugeland, J. (1985). Artificial intelligence: The very idea. Cambridge, Mass.: MIT Press.
  8. 8. Slide 8 Artificial Intelligence – Machine Learning – Deep Learning Copeland (2016): https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence- machine-learning-deep-learning-ai/
  9. 9. Folie 9 Hinojo-Lucena et al. (2019, S. 1): § "This technology [AI] is already being introduced in the field of higher education, although many teachers are unaware of its scope and, above all, of what it consists of." Hinojo-Lucena, F.-J., Aznar-Díaz, I., Cáceres-Reche, M.-P., & Romero-Rodríguez, J.-M. (2019). Artificial Intelligence in Higher Education: A Bibliometric Study on its Impact in the Scientific Literature. Education Sciences, 9(1), 51. https://doi.org/10.3390/educsci9010051 So… what is AI in Education (AIEd)?
  10. 10. Folie 11 § learner-facing (e.g. adaptive LMS or ITS) § teacher-facing (e.g. assessment and plagiarism detection tools) § system-facing AIEd (e.g. monitoring tools on institutional level) AI in Education (AIEd) Baker, T., & Smith, L. (2019). Educ-AI-tion Rebooted? Exploring the future of artificial intelligence in schools and colleges. Nesta Foundation.
  11. 11. Systematic Review on AIEd in HE
  12. 12. Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 39. https://doi.org/10.1186/s41239-019-0171-0 Downloaded over 18k times since October 2019
  13. 13. Zawacki-Richter, O., Kerres, M., Bedenlier, S., Bond, M., & Buntins, K. (Eds.). (2019). Systematic reviews in educational research: Methodology, perspectives and application. Heidelberg: Springer Open, Verlag für Sozialwissen- schaften.
  14. 14. Folie 15 Systematic Review Questions § Mapping: How have publications on AI in higher education developed over time, in which journals are they published, and where are they coming from in terms of geographical distribution and the author's disciplinary affiliations? § Concept and Ethics: How is AI in education conceptualised and what kind of ethical implications, challenges and risks are considered? § Applications: What is the nature and scope of AI applications in the context of higher education?
  15. 15. Folie 16 Context of AIEd Studies § Higher Education (n = 146) § K-12 (n = 6) § Cont. Ed. / Corporate Training (n = 1) § Vocational Training (n = 1)
  16. 16. Folie 17
  17. 17. Folie 18 Only 13 papers (8.9%) by first authors with an Education background.
  18. 18. Folie 19 Student life-cycle (Reid, 1995): § administrative services (e.g. admission, counselling, library services) – 92 studies (63.0 %) § academic support services (e.g. assessment, feedback, tutoring) – 48 studies (32.8 %) § Six studies (4.1 %) covered both levels Reid, J. (1995). Managing learner support. In F. Lockwood (Ed.), Open and distance learning today (pp. 265–275). London: Routledge.
  19. 19. Folie 21 Profiling and prediction: admissions § Chen and Do (2014): "the accurate prediction of students' academic performance is of importance for making admission decisions as well as providing better educational services" (p. 18). § Acikkar and Akay (2009) predict admission decisions based on a physical ability test, scores in the National Selection and Placement Examination, and GPA § Cukurova University, Adana, Turkey § Accuracy: 97% in 2006 using SVM
  20. 20. Folie 22 Intelligent Tutoring Systems (ITS) § Meta-analysis of 39 ITS studies (Steenbergen-Hu & Cooper, 2014): o ITS had moderate positive effect on college students' learning o ITS were less effective than human tutoring o but ITS outperformed all other instruction methods (traditional classroom instruction, reading printed or digital text, CAI, laboratory or homework assignments, and no-treatment control) § Simulation of one-to-one tutoring – enormous potential for ODE o make decisions about the learning path and content, o provide cognitive scaffolding and o help, to engage the student in dialogue. Steenbergen-Hu, S., & Cooper, H. (2014). A meta-analysis of the effectiveness of intelligent tutoring systems on college students’ academic learning. Journal of Educational Psychology, 106(2), 331–347.
  21. 21. Folie 23 Duolingo chat bots for language learning
  22. 22. Folie 24 Assessment and evaluation § Automated Essay Scoring (AES): Machine learning for assignment classification, grading § Practical for large courses due to the need to calibrate the system with pre-scored assignments (supervised machine learning) § Research focused on undergraduate courses (n = 10)
  23. 23. Folie 25 Automated Essay Scoring (Gierl et al., 2014) § AES for large scale assessment § Medical Council of Canada Qualifying Examination § Clinical Decision Making Constructed-Response Items (CDM-DCR) § 5.540 students took the test in 2013 § 100 raters need 4 days (2.800 to 3.200 hours) to score the items Gierl, M. J., Latifi, S., Lai, H., Boulais, A., & Champlain, A. (2014). Automated essay scoring and the future of educational assessment in medical education. Medical Education, 48(10), 950–962.
  24. 24. Folie 26 Automated Essay Scoring (Gierl et al., 2014) § Structure of CDM short-answer write-in questions
  25. 25. Folie 27 Automated Essay Scoring (Gierl et al., 2014) § LightSIDE software (open source) for machine learning classification § Agreement between 97.3 and 98.2 % § Kappa values: almost perfect agreement § Required 3.5 hours of one rater of coding to prepare the data, and 10 s to score the 2013 examination
  26. 26. Folie 28 Automated Essay Scoring § Commercial software § IntelliMetric: http://www.intellimetric.com
  27. 27. Folie 29 Adaptive systems and personalisation § Kose & Arslan (2016) developed and evaluated an "Intelligent E- Learning System" for Computer Programming courses at Usak University in Turkey § System based on ANN that is able to evaluate students’ responses on applications and predict their learning levels on different aspects of computer programming. § Based on student's learning level values appropriate content is provided to support student's learning Kose, U., & Arslan, A. (2016). Intelligent E-Learning System for Improving Students’ Academic Achievements in Computer Programming Courses. International Journal of Engineering Education, 32(1), 185–198.
  28. 28. Folie 30 CENTURY Intelligent Learning Platform § "CENTURY is the first teaching and learning platform to use AI. Our technology, named CAI, provides students with a truly personalised education and enables teachers to make evidence- based interventions." § https://www.century.tech/the-platform/
  29. 29. Folie 31 Ethical issues and challenges Only 2 out of 146 included studies discussed ethical issues associated with AIEd applications! "All AI researchers should be concerned with the ethical implications of their work" Russel & Norvig (2010, p. 1020) Russell, S. J., & Norvig, P. (2010). Artificial intelligence: A modern approach (3rd ed). Upper Saddle River: Prentice Hall.
  30. 30. Folie 32 Intelligent Classroom Behavior Management System https://www.businessinsider.de/china-school-facial-recognition-technology-2018-5?r=US&IR=T
  31. 31. Folie 33 Conclusion and implications for ODE § Huge student numbers in ODE, learning and teaching facilitated by digital media = BIG DATA § High potential of AIEd applications along the student life-cycle o Admissions, administrative services o Intelligent student support systems o Assessment for large classes: Calibration of AES systems § Overcoming the dilemma of economies of scale and personalized learning and teaching in ODE?
  32. 32. Folie 34 Conclusion and implications for ODE § Dramatic lack of concern of ethical issues § Driven by computer scientists (especially from China) § Misunderstandings about the nature of learning (behaviourist approaches: present – test – feedback) Lynch, J. (2017). How AI Will Destroy Education. https://buzzrobot.com/how-ai-will-destroy-education-20053b7b88a6.
  33. 33. Folie 35 Conclusion and implications for ODE § Gap between expectations and reality § Current state of AI in higher education is disappointing, no application in higher education on a broader scale "…there is little evidence at the moment of a major breakthrough in the application of ‘modern’ AI specifically to teaching and learning, in higher education, with the exception of perhaps learning analytics." (Bates et al., 2020, p. 4) Bates, T., Cobo, C., Mariño, O., & Wheeler, S. (2020). Can artificial intelligence transform higher education? International Journal of Educational Technology in Higher Education, 17(1), https://doi.org/10.1186/s41239-020-00218-x
  34. 34. Prof. Dr. Olaf Zawacki-Richter Carl von Ossietzky Universität Oldenburg Center for Lifelong Learning (C3L) Center for Open Education Research (COER) olaf.zawacki.richter@uni.oldenburg.de @Zawacki_Richter http://www.uni-oldenbur.de/coer/ Thanks for your attention!

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