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 and autonomous robots. Research about AI in higher education is widespread in the global north, and there is much hype from venture capital and big tech about revolutionising education with machines and their ability to understand natural language or improve reasoning[1] There is at present, no scientific consensus on what Ai is or how to classify and sub-categorize AI[2] [3] This has not hampered the growth of Ai 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. [4] Ai brings conversational coherence to the classroom, through categorisation, summaries and dialogue and its "intelligence" or "authority" is reinforced through anthropomorphism and the Eliza effect. Ai also introduces hazards and harmful educational practices.[5] Worries about risks such as privacy breaches, algorithmic biases, security concerns, ethics, compliance barriers are accompanied by other doomsday warnings.
Educational technology can be a powerful and effective assistants for learning. Computer companies are constantly updating their technology products. Some educationalists have suggested that Ai might automate procedural knowledge and expertise[6] 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.[7] Others are more skeptical as Ai faces an ethical challenge, where "fabricated responses" or "inaccurate information", politely referred to as “hallucinations” [8] 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[9] that leads towards determinist thinking.[10] While others see copyright infringement[11] or the introduction of harm, division and other social impacts, and advocate resistance to Ai.[12]
Large language models (LLMs) are statistical models, trained by billions of words and code that has been web-scraped. LLMS are feats of engineering, that see text as tokens. The statistical 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.[13] 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,[14] that is normalized and predictable. However, the text corpora that LLMs draw on can be problematic, as outputs will reflect their stereotypes or biases. 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.[15] "Guardrails" offer to act as validators of the LLM output, prevent these errors, and safeguard accuracy[16]
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” will transform education.[17] 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[18] and a collective vision for the future.[19] Improvements in natural language processing and computational linguistics have re-enforced assumptions that underlie this current STI. Ai appears to understand instructions and can can generate human-like responses.[20] Perfect companions for a lonely and alienated world.[21]
At first glance, Artificial intelligence in Education (AIEd)[22] does indeed offer pertinent technical solutions to address future education needs. 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. [23] AI skeptics on the other hand, are wary of rhetoric that presents technology as solution. They point out that only big tech can position their platforms as efficient tools or products to address educational challenges. [24] Post digital scholars and sociologists are more cautious about any techno-solutions, and have warned about the dangers of building public systems around alchemy [25] or stochastic parrots. They argue that there are multiple costs that accompany LLMs, including dangerous biases the potential for deception, and environmental costs[26] 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 [27]