Shaping Our Algorithms Before They Shape Us
- 664 Downloads
A common refrain among teachers is that they cannot be replaced by intelligent machines because of the essential human element that lies at the centre of teaching and learning. While it is true that there are some aspects of the teacher–student relationship that may ultimately present insurmountable obstacles to the complete automation of teaching, there are important gaps in practice where artificial intelligence (AI) will inevitably find room to move. Machine learning is the branch of AI research that uses algorithms to find statistical correlations between variables that may or may not be known to the researchers. The implications of this are profound and are leading to significant progress beign made in natural language processing, computer vision, navigation and planning. But machine learning is not all-powerful, and there are important technical limitations that will constrain the extent of its use and promotion in education, provided that teachers are aware of these limitations and are included in the process of shepherding the technology into practice. This has always been important but when a technology has the potential of AI we would do well to ensure that teachers are intentionally included in the design, development, implementation and evaluation of AI-based systems in education.
KeywordsArtificial intelligence Machine learning Educational technology
- Agrawal, A., Gans, J., & Goldfarb, A. (2018). Prediction machines: The simple economics of artificial intelligence. Boston: Harvard Business Review Press.Google Scholar
- American Medical Association. (2018). AMA passes first policy recommendations on augmented intelligence. Available from https://www.ama-assn.org/ama-passes-first-policy-recommendations-augmented-intelligence.
- Andreesson, M. (2011). Why software is eating the world. Available at https://a16z.com/2016/08/20/why-software-is-eating-the-world/.
- Bostrom, N., & Yudkowsky, E. (2014). The ethics of artificial intelligence. In K. Frankish & W. M. Ramsay (Eds.), The Cambridge handbook of artificial intelligence (pp. 316–334).Google Scholar
- Brynjolfsson, E., & McAfee, A. (2014). The second machine-age: Work, progress, and prosperity in a time of brilliant technologies. New York: W.W. Norton & Company.Google Scholar
- Frankish, K., & Ramsay, W. M. (2017). The cambridge handbook of artificial intelligence. Cambridge: Cambridge University Press.Google Scholar
- Freire, P. (2005). Pedagogy of the oppressed. 30th anniversary edition. Continuum. London: The Continuum International Publishing Group Ltd.Google Scholar
- Giroux, H. (2011). On critical pedagogy. Continuum. London: The Continuum International Publishing Group Ltd.Google Scholar
- Hart, R. D. (2017). If you’re not a white male, artificial intelligence’s use in healthcare could be dangerous. Quartz. Available at https://qz.com/1023448/if-youre-not-a-white-male-artificial-intelligences-use-in-healthcare-could-be-dangerous/.
- Hendrick, C. (2018). Challenging the ‘education is broken’ and Silicon Valley narratives. ResearchED. Available at https://researched.org.uk/challenging-the-education-is-broken-and-silicon-valley-narratives/.
- Hooks, B. (1994). Teaching to transgress. Education as the Practice of Freedom. New York: Routledge, Taylor & Francis Group.Google Scholar
- Jordan, M. (2018). Artificial intelligence—The revolution hasn’t happened yet. Medium. Available at https://medium.com/@mijordan3/artificial-intelligence-the-revolution-hasnt-happened-yet-5e1d5812e1e7.
- Kahneman, D. (2011). Thinking fast, and slow. Straus and Giroux: Farrar.Google Scholar
- Lyell, D., & Coiera, E. (2017). Automation bias and verification complexity: A systematic review. Journal of the American Medical Informatics Association, 24(2), 423–431.Google Scholar
- Lynch, J. (2018). How AI will destroy education. Medium. Available at https://buzzrobot.com/how-ai-will-destroy-education-20053b7b88a6.
- Mittelstadt, B. D., Allo., P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society.Google Scholar
- Morris, S. M., & Stommel, J. (2017). Open education as resistance: MOOCs as critical digital pedagogy. In E. Losh (Ed.), MOOCs and their afterlives: Experiments in scale and access in higher education. London: University of Chicago Press.Google Scholar
- Pearl, J., & Mckenzie, D. (2018). The book of why: The new science of cause and effect. New York: Basic Books.Google Scholar
- Polanski, V. (2016). Would you let an algorithm choose the next US president? World Economic Forum. Available at https://www.weforum.org/agenda/2016/11/would-you-let-an-algorithm-choose-the-next-us-president/.
- Ritchie, S. (2016). Intelligence: All that matters. Teach yourself. United Kingdom: Hachette.Google Scholar
- Searle, J. (2011). Watson doesn’t know it won on ‘Jeopardy!’. Wall Street Journal. Retrieved on September 27, 2018 from https://www.wsj.com/articles/SB10001424052748703407304576154313126987674.
- Siemens, G. (2005). Connectivism: A learning theory for the digital age. International Journal of Instructional Technology and Distance Learning, 2(1), 3–10.Google Scholar
- Susskind, R., & Susskind, D. (2015). The future of the professions: How technology will transform the work of human experts. Oxford: Oxford University Press.Google Scholar
- Watters, A. (2015). Teaching machines and turing machines: The history of the future of labor and learning. Available at http://hackeducation.com/2015/08/10/digpedlab.
- Williamson, B. (2018). The tech elite is making a power-grab for public education. Code Acts in Education. Available at https://codeactsineducation.wordpress.com/2018/09/14/new-tech-power-elite-education/.