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Artificial intelligence or Ai is a broad “skewer” term that has specific areas of study clustered next to it, including machine learning, natural language processing, the philosophy of artificial intelligence, autonomous robots and TESCREAL. Likewise, Ai in education (aied) also has a variety of areas of research, skewered together.[1] Including anthropomorphism, generative artificial intelligence, data-driven decision-making, ai ethics, classroom surveillance, data-privacy and Ai Literacy.

There are multiple and diverse understandings, practices, and perceptions around  AI in education. This complex social, cultural, and material assemblage should be seen in its geo-political context. Ai will be shaped by different policy or economic imperatives which will influence the construction, legitimation and use of Ai in an education setting.[2]

The AI in education community

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The Ai in education community has grown rapidly in the global north.[3] Currently, there is much hype from venture capital, big tech and convinced open educationalists. Ai in education is a contested terrain. Some educationalists believe that Ai will remove the obstacle of "access to expertise”.[4] Others claim that education will be revolutionised with machines and their ability to understand natural language.[5] While others are exploring how LLM’s “reasoning” might be improved.[6] While in the global south, others see the Ai's data processing and monitoring as a misguided attempt to address colonialism and apartheid, that that has inadvertently re-enforced a neo-liberal approach to education.[7]

Algorithms effects on education

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AI in education is currently preoccupied with Generative artificial intelligence (GAI), although data science and data analytics is another popular theme. At present, there is little scientific consensus on what Ai is or how to classify and sub-categorize AI[8][9] This has not hampered the growth of Ai in education systems, which offer scholars and students automatic assessment and feedback, predictions, instant machine translations, on-demand proof-reading and copy editing, intelligent tutoring or virtual assistants. [3] The "generative-AI supply chain",[10] brings conversational coherence to the classroom, and automates the production of content.[11]Using categorisation, summaries and dialogue, Ai "intelligence" or "authority" is reinforced through anthropomorphism and the Eliza effect.

Framing education

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Educational technology can be a powerful and effective assistant in a suitable setting. Computer companies are constantly updating their technology products. Some educationalists have suggested that Ai might automate procedural knowledge and expertise[12] or even match or surpass human capacities on cognitive tasks. They advocate for the integration of AI across the curriculum and the development of AI Literacy.[13] Others are more skeptical as Ai faces an ethical challenge, where "fabricated responses" or "inaccurate information", politely referred to as “hallucinations” [14] are generated and presented as fact. Some remain curious about societies tendency to put their faith in engineering achievements, and the systems of power and privilege[15] that leads towards determinist thinking.[16] While others see copyright infringement[17][18] [19] or the introduction of harm, division and other social impacts, and advocate resistance to Ai.[20] Evidence is mounting that Ai written assessments are undetectable, which poses serious questions about the academic integrity of university assessments.[21]

Tokens, text and hallucinations

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Large language models (LLMs) take text as input data and then generate output text.[22] Ai models are trained by billions of words and code that has been web-scraped. LLMS are feats of engineering, that see text as tokens. The relationships between tokens, allows LLM to predict the next word, and then the next, thus generating a meaningful sentence and the appearance of thought and interactions. LLM are often dependent on a huge text corpus that is normally extracted from the World Wide Web. This dataset allows the LLM to act as a statistical reasoning machine, [23] or do pattern recognition.[24] The LLM examines the relationships between tokens, generates probable outputs in response to a prompt, and completes a defined task, such as translating, editing, or writing. The output that is presented is a smoothed collection of words,[25] that is normalized and predictable. However, the text corpora that LLMs draw on can be problematic, as outputs will reflect their stereotypes or biases of the people or culture whose content has been digitized. The confident, but incorrect outputs are termed “hallucinations”. These plausible errors are not malfunctions but a consequence of the engineering decisions that inform the large language model.[26]"Guardrails" offer to act as validators of the LLM output, prevent these errors, and safeguard accuracy[27]There are no fixes[28][29] for so-called "hallucinations", the "factually incorrect or nonsensical information that seems plausible[30] Translation, summarization, information retrieval, conversational interactions are some of the complex language tasks that machines are expected to handle.[31]

Socio-technical imaginaries

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The benefits of multilingualism, grammatically correct sentences or statistically probable texts written about any topic or domain are clear to those who can afford software as a service (SaaS). In edtech, there is a recurrent theme, that “emerging technologies”[32] will transform education.[33] Whether it be radio, TV, PC computers, the internet, interactive whiteboards, social media, mobile phones or tablets. New technologies generate a socio technical imaginary (STI) that offer's society, a shared narrative[34] and a collective vision for the future.[35] Improvements in natural language processing and computational linguistics have re-enforced assumptions that underlie this “emerging technology” STI. Ai is not an emerging technology, but an “arrival technology”[36] Ai appears to understand instructions and can generate human-like responses.[37] Behaving as a companion for many in a lonely and alienated world.[38] While also creating a “jagged technology frontier”, [39]where Ai is both very good and terribly bad at very similar tasks.[40]

Public goods vs venture capital

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At first glance, artificial intelligence in education offers pertinent technical solutions to address future education needs.[41] AI optimists envision a future where machine learning and artificial intelligence might be applied in writing, personalization, feedback or course development. The growing popularity of AI, is especially apparent to many who have invested in higher education in the past decade. [42] AI skeptics on the other hand, are wary of rhetoric that presents technology as solution. They point out that in public services, like education, human and algorithmic decision systems should be approached with caution. [43] Post digital scholars and sociologists are more cautious about any techno-solutions, and have warned about the dangers of building public systems around alchemy [44] or stochastic parrots. They argue that there are multiple costs that accompany LLMs, including dangerous biases the potential for deception, and environmental costs[45] The AI curious are aware of how cognitive activity has become commodified. They see how education has been transformed into a “knowledge business” where items are traded, bought, or sold.[46] African hyper scalers, venture capital and vice chancellors[47] are punting the Fourth Industrial Revolution. with the prospect of billions earmarked for South African. Data Centers,[48] such as Teraco Data Environments, Vantage Data Centre,[8] Africa Data Centres[11] NTT /Dimension_Data,[5] carefully avoiding being accused of monopoly practices[49]

AI resilient graduates

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AI has co-existed comfortably between academia and industry for years.[50] The terrain is shifting and currently Ai research in the global north has computing power, large datasets, and highly skilled researchers. Power is shifting away from students and academics toward corporations and venture capitalists.[51] Graduates from universities in dominant cultures, where there are high levels of digitisation, need to become AI-resilient. Graduates from the majority world also need to value their own process of knowledge construction, resist the lure of normalisation and see Ai for what it is, another form of enclosure, and start blogging. Graduates from both the global north and the majority of the world need to be able to critique AI output, become familiar with ithe processes of technical change,[52] and let their own studies and intellectual life guide their working futures.[53]

Prominent digital education researchers and works on artificial intelligence in education

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Critical Sceptics Curious Practitioners Convinced Experts Committed Champions
Ben Williamson[54] Lance Eton[55] David Wiley [56]
Helen Beetham[57] Anna Mills[58] Stephen Downs
Bryan Alexander

It is human to categorize and then gravitate towards the published works of authors with whom we share similar thoughts and familiar ideas.[59] It is easy for champions or critics within the artificial intelligence in education community to ignore those in the opposite camps, instead of discussing differences of opinion and discovering matters of agreement.[60] The above spectrum is another "skewer", intended to introduce unfamiliar voices and their research to a wider readership, and work with the backlash. [opinion]

References

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