AI in education

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. Research about AI in higher education is widespread in the global north, where there is much hype from venture capital, big tech and some open educationalists. Some believe that Ai will remove the obstacle of "access to expertise”. Others claim that education will be revolutionized with machines and their ability to understand natural language. While others are exploring how LLM’s “reasoning” might be improved. Marketers often use different kinds of myths to make complex products seem simpler There is at present, no scientific consensus on what Ai is or how to classify and sub-categorize AI 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. Ai brings conversational coherence to the classroom, and automates the production of content.Using categorisation, summaries and dialogue, Ai "intelligence" or "authority" is reinforced through anthropomorphism and the Eliza effect. Ai also introduces hazards, such as skewed scientific knowledge dissemination , harmful educational practices, or might discourage researchers from publishing their original research openly. Worries about AI safety risks such as privacy breaches, algorithmic biases, security concerns, ethics, and compliance barriers are accompanied by other doomsday warnings.

Different Perspectives on Ai in Education

Educational technology can be a powerful and effective assistant for learning. Computer companies are constantly updating their technology products. Some educationalists have suggested that Ai might automate procedural knowledge and expertise 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. Others are more skeptical as Ai faces an ethical challenge, where "fabricated responses" or "inaccurate information", politely referred to as “hallucinations” 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 that leads towards determinist thinking. While others see copyright infringement or the introduction of harm, division and other social impacts, and advocate resistance to Ai. Evidence is mounting that Ai written assessments are undetectable, which poses serious questions about the academic integrity of university assessments.

Tokens, Text and Hallucinations

Large language models (LLMs) take text as input data and then generate output text. 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, or do pattern recognition. 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, 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."Guardrails" offer to act as validators of the LLM output, prevent these errors, and safeguard accuracyThere are no fixes for so-called "hallucinations", the "factually incorrect or nonsensical information that seems plausible Translation, summarization, information retrieval, conversational interactions are some of the complex language tasks that machines are expected to handle.

Socio-Technical Imaginaries (STI)

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. 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 and a collective vision for the future. 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” Ai appears to understand instructions and can generate human-like responses. Behaving as a companion for many in a lonely and alienated world. While also creating a “jagged technology frontier”, where Ai is both very good and terribly bad at very similar tasks.

Public Goods vs Venture Capital

At first glance, Artificial intelligence in Education (AIEd) 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. 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. Post digital scholars and sociologists are more cautious about any techno-solutions, and have warned about the dangers of building public systems around alchemy or stochastic parrots. They argue that there are multiple costs that accompany LLMs, including dangerous biases the potential for deception, and environmental costs 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. African hyper scalers, venture capital and vice chancellors are punting the Fourth Industrial Revolution. with the prospect of billions earmarked for South African. Data Centers, such as Teraco Data Environments, Vantage Data Centre, Africa Data Centres NTT /Dimension_Data, carefully avoiding being accused of monopoly practices

AI Resilient Graduates

AI has co-existed comfortably between academia and industry for years. 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. 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, and let their own studies and intellectual life guide their working futures.