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AI & SOCIETY

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09.08.2020 | Open Forum

Out of the laboratory and into the classroom: the future of artificial intelligence in education

verfasst von: Daniel Schiff

Erschienen in: AI & SOCIETY | Ausgabe 1/2021

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Abstract

Like previous educational technologies, artificial intelligence in education (AIEd) threatens to disrupt the status quo, with proponents highlighting the potential for efficiency and democratization, and skeptics warning of industrialization and alienation. However, unlike frequently discussed applications of AI in autonomous vehicles, military and cybersecurity concerns, and healthcare, AI’s impacts on education policy and practice have not yet captured the public’s attention. This paper, therefore, evaluates the status of AIEd, with special attention to intelligent tutoring systems and anthropomorphized artificial educational agents. I discuss AIEd’s purported capacities, including the abilities to simulate teachers, provide robust student differentiation, and even foster socio-emotional engagement. Next, to situate developmental pathways for AIEd going forward, I contrast sociotechnical possibilities and risks through two idealized futures. Finally, I consider a recent proposal to use peer review as a gatekeeping strategy to prevent harmful research. This proposal serves as a jumping off point for recommendations to AIEd stakeholders towards improving their engagement with socially responsible research and implementation of AI in educational systems.

Vorheriger Artikel Artificial intelligence and moral rights

Nächster Artikel Do robots dream of escaping? Narrativity and ethics in Alex Garland’s Ex-Machina and Luke Scott’s Morgan

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See for example Nicholas Negroponte, founder of MIT’s media Lab, whose ambitious One Laptop per Child program did not achieve its goal of reaching hundreds of millions of students and transforming global education (Warschauer and Ames 2010).

See the idea of “threat rigidity” when teachers face a constant stream of externally imposed reforms (Olsen and Sexton 2009).

Marvin Minsky predicted that before 1980, “we will have a machine with the general intelligence of an average human being.” Herbert Simon offered that by 1985, “machines will be capable of doing any work a man can do.”

Woolf et al. also follow in this more sober tradition for predictions and challenges facing AIEd (Woolf et al. 2013) as does the US Department of Education working with American Institutes for Research. For example: “As with all education technology tools, however, the benefits of intelligent tutoring systems will be realized only if they are equitably accessed and effectively integrated into and coordinated with complementary learning activities such as group discussions and project-based learning” (Tanenbaum 2016).

For a few excellent overviews, see Ely (1999); Leggett and Persichitte (1998), Romiszowski (2004), Nye (2015).

A calculator can be used for mathematics, storing information, playing games, sharing silly messages, and even making music, but it is not particularly helpful for viewing microscopic organisms.

More skeptically, when Dede argued that “even given the optimistic technological assumptions,” AIEd would “have no significant potential for standalone ‘education’” (Dede 1988). This very much remains to be seen, as much has already changed in the capabilities of AI.

This operational definition avoids semantic and technical debates about what defines AI. Readers can refer to Krafft et al. (2020) for alternative definitions.

Replace “intelligent tutoring systems” with “intelligent teaching systems” and the impetus is obvious.

Subsequently though, the use of explicitly emotional educational agents raises a cascade of ethical and design issues related to student privacy and manipulation. Do we want educational software to shame students for not working hard enough if it is an effective strategy, or to use gamification-style rewards (bright colors and addictive structures) to engender engagement? I consider this in the next section.

Payr offers the idea of agents as actors, used in simulations for healthcare, management, etc., towards educational purposes. I restrict my attention to more classic instructional agents.

We might greatly be concerned about displacing and disempowering teachers, but lowering marginal costs is not necessarily bad, and technological destruction and creation can be for the ultimate good.

Research about the failure of MOOCs in India contrasted against the success of local technical institutes reveals that Indian students have different learning styles, a need for career relevance, and technological challenges with navigating digital educational tools as compared to the presumed (Western) consumers of MOOCs (Joshi et al. 2018).

Even within STEM, some content and some courses are more easily modeled than others.

The validity of grades and test scores as measures of effective teaching/learning in contrast with various social–emotional/cognitive/twenty-first-century skills and long-term outcomes is beyond the scope here. Even these latter outcomes for ITS are within the realm of possibility now.

The relative effect sizes were calculated as a Cohen’s d of .76 for ITS and .79 for human tutors.

Teachers are simultaneously some of the most respected and least empowered individuals in society (Gallup 2010; Ingersoll and Perda 2008). Teachers are expected to be able to solve educational problems even without necessary support and resources and are nevertheless blamed when they fail to do so.

See the XSEED program which tries to leverage and improve human capacity while maintaining scalability through highly packaged education (Nayak et al. 2010).

This is somewhat analogous to the concept of premature deindustrialization (Rodrik 2016).

I would not be the first person to observe that STEM has captured education policy with talks of gaps, pipelines, and crises (Metcalf 2010).

Dreyfus suggested that social functions like shame are common and indeed useful in education. Does the same apply when a computer is doing the shaming?

Of note, we accept many of these nudging practices in children’s books, television shows, in classrooms, and on cereal boxes. It is important to uncover what the ethical lines are and what makes AI applications different, if anything. Consider the recent energy behind gamification and related criticisms (Rughinis 2013).

Nuclear technology is a relatively easier case, for example. In general, knowing where to draw the lines of responsibility can be very challenging, especially given the problem of many hands (Thompson 1980).

An exception is the case where lines of code included in the research are readily corruptible towards harmful purposes. In this case, disclosure to developers or authorities or withholding code is an established approach in computer security (Waddell 2018a).

A great exemplar of this is the International Journal of Artificial Intelligence in Education, which published a series of interdisciplinary discussions on the future of AIEd in a special edition (Lane et al. 2016).

A promising example is that of authoring tools, which allowed nonexperts to have some say in creating their own AIEd platforms (Dermeval et al. 2017). However, authoring tools have a long way to go before they are truly accessible to teachers and other curriculum developers.

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Titel: Out of the laboratory and into the classroom: the future of artificial intelligence in education
verfasst von: Daniel Schiff
Publikationsdatum: 09.08.2020
Verlag: Springer London
Erschienen in: AI & SOCIETY / Ausgabe 1/2021
Print ISSN: 0951-5666
Elektronische ISSN: 1435-5655
DOI: https://doi.org/10.1007/s00146-020-01033-8

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