Blogging an Unpublished Paper: South African & Egyptian Academic Developers’ Perceptions of AI in Education Part 1: Abstract & References

Estimated reading time: 3 minutes, 30 seconds

In case you missed my last post, I am blogging an unpublished paper as a series in parts over several days. You can read that post to understand the story and the reason behind this. Comments on each post welcome. This is the first post, and it will have the abstract and references. I added something between brackets in the abstract.

Abstract

The potential uses of Artificial Intelligence and particularly Machine Learning in education has been gaining traction in media. We often hear the perspectives of the designers, often computer scientists or corporations, and more recently, we hear groups working towards a more ethical AI, often at large research institutions like MIT, Harvard and Stanford. But how do educators use AI and how do they perceive other potential uses? There is a gap in the literature that tackles educators’ experiences on the ground, to see whether AI indeed solves problems or creates opportunities for enhancing teaching and learning. This article will focus on three applications of machine learning in higher education: plagiarism-detection systems such as Turnitin.com, automated grading, and teacher bots, and two others, speech recognition and automated translation. Faculty/academic developers at two African universities (in Egypt and South Africa) were interviewed about their actual experiences with Turnitin.com, its benefits, costs and challenges, from both a pedagogical and logistical perspective. They were also asked about their attitudes and perceptions towards the potential of teacher bots, automated grading, speech recognition and automated translation, and what it means for the future of the teaching profession. They were also asked explicitly about ethical issues in AI and how this influences their perception of the future of AI in education.

References

Bali, M. (2017, November 29). Against the 3 A’s of EdTech: AI, analytics and adaptive
technologies in education. Prof Hacker blog. Retrieved from:
https://www.chronicle.com/blogs/profhacker/against-the-3as-of-edtech-ai-analytics-and-adaptive
-technologies-in-education/64604

Bayne, S. (2015) Teacherbot: interventions in automated teaching, Teaching in Higher Education, 20:4, pp. 455-467, DOI: 10.1080/13562517.2015.1020783

Bayne, S., Evans, P., Ewins, A., Knox, J., Lamb, J., Macleod, H., … Sinclair, C. (2016). Manifesto for Teaching Online 2016. Retrieved from:
https://www.research.ed.ac.uk/portal/en/publications/manifesto-for-teaching-online-2016(bec579
ea-3a3a-4064-ba14-44dda42feb40).html

Buolamwini, J. (2016, November). I’m fighting bias in algorithms. TED Talk. Retrieved from:
https://www.ted.com/talks/joy_buolamwini_how_i_m_fighting_bias_in_algorithms?language=en

Denzin, N. and Lincoln, Y. S. (2005). Introduction: the discipline and practice of qualitative research. In N. Denzin and Y. S. Lincoln (Eds.), The SAGE handbook of qualitative research, 3rd edition (pp. 1-32), Thousand Oaks, CA; London, England and New Delhi, India: Sage.

Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence In Education: Promises and
Implications for Teaching and Learning.

Li, J., Link, S., & Hegelheimer, V. (2015). Rethinking the role of automated writing evaluation
(AWE). Journal of Second Language Writing. 27, pp. 1018. DOI:
http://dx.doi.org/10.1016/j.jslw.2014.10.004

Morris, S. M., & Stommel, J. (2017, June 15). A guide for resisting edtech: The case against Turnitin. Hybrid Pedagogy. Retrieved from: https://hybridpedagogy.org/resisting-edtech/


Noble, S. U. (2018). Algorithms of Oppression: How search engines reinforce racism. NYU Press: New York, NY.

O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and
threatens democracy. Broadway Books.

Puentedura, R. (2015, October.) SAMR: A brief introduction [web log post]. Retrieved from:
http://hippasus.com/blog/archives/227

Robitzski, D. (2018, October 18). You Have No Idea What Artificial Intelligence Really Does: The world of AI is full of hype and deception. Futurism. Retrieved from:
https://futurism.com/artificial-intelligence-hype

Tufekci, Z. (2016, June). Machine intelligence makes human morals more important. TED Talk. Retrieved from:
https://www.ted.com/talks/zeynep_tufekci_machine_intelligence_makes_human_morals_more_important

Tufecki, Z. (2018, August 14). How social media took us from Tahrir Square to Donald Trump.
MIT Technology Review. Retrieved from:
https://www.technologyreview.com/s/611806/how-social-media-took-us-from-tahrir-square-to-donald-trump/


Usher, R. (1996/2001). A critique of the neglected epistemological assumptions of educational research. In D. Scott and R. Usher (eds), Understanding Educational Research (pp. 9-32). London, England and New York, NY: Routledge. [eBook Taylor & Francis e-Library]. Retrieved from http://find.shef.ac.uk


Watters, A. (2015). Retrieved from: http://hackeducation.com/2015/08/10/digpedlab

That’s it for now – wanna suggest more references? Leave them in the comments?

Featured image from Pixabay

Leave a Reply

This site uses Akismet to reduce spam. Learn how your comment data is processed.

Follow

Get every new post on this blog delivered to your Inbox.

Join other followers:

%d bloggers like this: