Skip to main content

AI and education, the questions not being asked

 


Selena Nemorin, Andreas Vlachidis, Hayford M. Ayerakwa, and Panagiotis Andriotis explore the hype surrounding AI in education and provide a horizon scan of the current discourse. 'AI hyped? A horizon scan of discourse on artificial intelligence in education (AIED) and development'.

This paper provides a horizon scan of the discourse surrounding artificial intelligence (AI) in education and development. The authors use text mining and thematic analysis to explore the hype surrounding AI in education and identify key themes that have emerged during the AIEd debate. The findings are categorized into three themes: geopolitical dominance through education and technological innovation, creation and expansion of market niches, and managing narratives, perceptions, and norms. The paper highlights the challenges of implementing AI in educational settings due to a lack of rigorous evidence supporting practical outcomes.

One of the key themes identified in the paper is geopolitical dominance through education and technological innovation. The authors suggest that AI in education could be used as a tool for countries to gain geopolitical power and influence. They argue that this could lead to new forms of colonialism, where powerful countries use their technological advantage to dominate and control other nations. The paper highlights the need for critical reflection on the potential implications of AI in education for global power dynamics.

The authors suggest that AI in education is being driven by market forces, with technology companies seeking to profit from the development and implementation of AI-powered educational tools. They also highlight the importance of managing public perceptions and narratives around AI in education, as these can shape policy decisions and funding priorities. 

It is of note, and concerning, that the debate around AI in education is being framed by many transnational institutions in the manner it is:

Positive rhetoric can be found in the discourse of global governance institutions. For instance, Audrey Azoulay, the Director-General of UNESCO (2019), claims: ‘Education will be profoundly transformed by AI … Teaching tools, ways of learning, access to knowledge, and teacher training will be revolutionized.

The authors note: 

At the core of many current AI-driven educational initiatives lies a computational understanding of education and learning that reduces student and teacher lifeworlds to sets of data logics that can be managed and understood. Underpinned by instrumental rationality and a desire for mechanisation and control of bare life (zoe), these processes of datafication (van Dijck, 2014) seek to make schooling as a lived practice knowable, predictable, and thereby governable. The datafication of education in discourse is aligned with positivist thinking and reductionist impulses. 

 AI-neurological methods of connecting students to techniques of measurement and addictive technologies are also pushing boundaries of bodily sovereignty in the sense that surveillant technologies are increasingly intruding into the integrity of the human body and fundamental human rights to cognitive freedom—the right to mental self-determination. All aspects of the child being measured to analyse and know its niche behaviours. Aspects of the child are then represented as educational and marketing materials and confined to a learning world that is decided upon by an artificial intelligence through strategies of personalisation.

I am reminded of when I was a researcher for the British Education Computing and Technology Association, where government at the time where obsessed with the education approaches being conducted in Singapore. Whilst Singapore, on first glance, had a 'celebrated' education system known for its robust curriculum and excellent results. It is also a system of keen competitiveness and high pressure, with a narrow curriculum designed specifically for employment and employers. AI poses various threats to education, with many, perhaps, unintended consequences, as was the case when seeking to impose a Singapore model upon a Western European education system. The quality of many papers exploring what these may be, is sadly lacking. This paper helps bridge that gap.

The authors conclude:

 At the heart of these AI tools and principles are how Western ideas are being superimposed on countries in the Global South evidenced by the aggressive policies and strategies being adopted to expand geopolitical dominance through AI. 

This seems to get to the heart of many questions AI poses to societies. 

 

Labels:

Comments

Post a Comment

Popular posts from this blog

The AI Dilemma and "Gollem-Class" AIs

From the Center for Humane Technology Tristan Harris and Aza Raskin discuss how existing A.I. capabilities already pose catastrophic risks to a functional society, how A.I. companies are caught in a race to deploy as quickly as possible without adequate safety measures, and what it would mean to upgrade our institutions to a post-A.I. world. This presentation is from a private gathering in San Francisco on March 9th with leading technologists and decision-makers with the ability to influence the future of large-language model A.I.s. This presentation was given before the launch of GPT-4. One of the more astute critics of the tech industry, Tristan Harris, who has recently given stark evidence to Congress. It is worth watching both of these videos, as the Congress address gives a context of PR industry and it's regular abuses. "If we understand the mechanisms and motives of the group mind, it is now possible to control and regiment the masses according to our will without their
Post a Comment
Read more

Beware the Orca, the challenge to ChatGPT and Palm2 is here

  So Google's 'we have no moat' paper was correct. If you train an LLM wisely then it's cost effective and cheap to produce a small LLM that is able to compete or even beat established, costly LLMs, as Microsoft has just found. It's another excellent video from AI Explained, who goes through some of the training procedures, which I won't get into here. Orca, is a model that learns from large foundation models (LFMs) like GPT-4 and ChatGPT by imitating their reasoning process. Orca uses rich signals such as explanations and complex instructions to improve its performance on various tasks. Orca outperforms other instruction-tuned models and achieves similar results to ChatGPT on zero-shot reasoning benchmarks and professional and academic exams. The paper suggests that learning from explanations is a promising way to enhance model skills. Smaller models are often overestimated in their abilities compared to LFMs, and need more rigorous evaluation methods. Explana
Post a Comment
Read more

What is happening inside of the black box?

  Neel Nanda is involved in Mechanistic Interpretability research at DeepMind, formerly of AnthropicAI, what's fascinating about the research conducted by Nanda is he gets to peer into the Black Box to figure out how different types of AI models work. Anyone concerned with AI should understand how important this is. In this video Nanda discusses some of his findings, including 'induction heads', which turn out to have some vital properties.  Induction heads are a type of attention head that allows a language model to learn long-range dependencies in text. They do this by using a simple algorithm to complete token sequences like [A][B] ... [A] -> [B]. For example, if a model is given the sequence "The cat sat on the mat," it can use induction heads to predict that the word "mat" will be followed by the word "the". Induction heads were first discovered in 2022 by a team of researchers at OpenAI. They found that induction heads were present in
Post a Comment
Read more