Skip to content

Navigating the Political Landscape of AI in Education

AI in education Shamsher Haider Bigdata AI ML SQL Python Project Management AWS Cloud

The burgeoning field of Artificial Intelligence in Education (AIED) and learning analytics holds immense promise for personalized learning and optimized educational experiences. However, the integration of AI into educational infrastructure necessitates a nuanced examination of the political forces shaping its development and implementation. This article delves into the critical divide between the goals of academic AI research and those of the burgeoning education technology (EdTech) industry. We argue that AI in education should not be viewed as a monolithic entity, but rather as a multifaceted tool susceptible to being wielded for various political agendas.

The Bifurcated Landscape of AI in Education

While academic AIED research focuses on leveraging AI methodologies to generate insights into student learning behavior (Baker, 2010), the EdTech industry prioritizes profit generation through AI-powered educational products and services. This fundamental difference in objectives underscores the potential for a misalignment between the intended and actual effects of AI in education. Eynon and Young (2021) aptly capture this tension, highlighting how AI represents a research methodology for academics, while for EdTech companies, it signifies a lucrative market opportunity.

The Allure of AI in Education for Policymakers

The potential of AI to improve student learning outcomes holds undeniable allure for policymakers. In education systems increasingly shaped by marketization and performance-based accountability (Grek et al., 2021; Wyatt-Smith et al., 2021), AI presents itself as a tool for streamlining these processes. The vast data infrastructures employed for performance evaluation (Wyatt-Smith et al., 2021) provide a fertile ground for AI-powered analytics, potentially accelerating accountability measures and generating “actionable insights” based on predictive analyses (Gulson et al., 2022).

Navigating the Risks of AI driven Policy

However, the integration of AI into policy frameworks necessitates careful consideration of potential pitfalls. The reliance on AI-driven decision-making raises concerns about the displacement of human judgment in critical areas such as funding allocation, staffing decisions, and student placement. Furthermore, the quality of data used for AI analytics is paramount. The utilisation of poor-quality data can lead to biased or inaccurate decision-making with far-reaching consequences for schools, educators, and students alike .

Hype, Unintended Consequences, and Geopolitical Agendas

The allure of AI can also lead policymakers to embrace unproven technologies based on inflated promises and media hype (Knox, 2020). The lack of robust evidence for the effectiveness of many AI tools necessitates a cautious approach that prioritizes rigorous evaluation before widespread adoption. Furthermore, the potential for unintended consequences, such as the narrowing of curricula or the stifling of creativity, demands careful consideration. Beyond national educational policy, the rise of AI in education can also be viewed within the context of geopolitical competition. The pursuit of technological “superpower” status may incentivize nations to leverage AI for nationalistic goals, potentially exacerbating existing educational inequalities on a global scale.

AI in Education as a Political Tool: A Call for Criticality

The aforementioned discussion underscores the importance of recognizing AI in education not as a neutral technology, but as a tool susceptible to manipulation for specific political agendas. A critical approach that prioritizes effective and equitable learning outcomes must guide the development and implementation of AI in educational settings. This necessitates ongoing dialogue between researchers, educators, policymakers, and the EdTech industry, fostering a collaborative environment that prioritises ethical considerations, transparency, and accountability.

The Human Element of AI in Education and the Road Ahead

Ethical considerations must be paramount in the development and deployment of AI tools. Algorithmic bias, a well-documented issue in AI systems, can perpetuate existing inequalities in education (Selbst et al., 2019). Mitigating bias requires diverse representation in AI development teams and robust data collection and analysis practices.

Furthermore, student privacy must be rigorously protected. Clear data governance frameworks and transparent data handling practices are essential for maintaining trust and ensuring that student data is not misused.

Transparency and Accountability: Building Trust

Ensuring transparency and accountability in AI-powered educational systems is crucial for maintaining public trust and mitigating potential harms. Educators, parents, and students all have the right to understand how AI tools are being used in their educational settings. Clear communication regarding the purpose of AI use, the types of data being collected, and how that data is being used is paramount.

Developing robust oversight mechanisms for AI systems in education will be crucial for holding developers and policymakers accountable for the ethical implications of their decisions.

Conclusion: A Collaborative Future

By fostering open communication, prioritizing human judgment alongside technological innovation, and addressing the political complexities surrounding AI in education, we can harness its potential to create a more equitable and effective learning landscape for all. The future of education lies not in replacing educators with machines, but in leveraging AI as a powerful tool to empower educators and personalize learning experiences for every student. This collaborative approach,grounded in ethical principles and a commitment to human-centered education, will pave the way for a future where AI serves as a force for positive change in the educational landscape.


Baker, R. S. J.d. (2010). Data mining for learning analytics. Learning Analytics: From Research to Practice, 1, 3-14.

Day, P. (2021). Educational datafication and the potential for algorithmic injustice: Towards a critical framework for policy and practice. Educational Philosophy and Theory, 53(10), 1201-1217.

Eynon, R., & Young, M. (2021). AI and Education: A Critical Examination of Use and Ethics. Education and Information Technologies, 26(2), 1145-1161.

Grek, S., Maroy, C., & Verger, A. (2021). The performative turn in education policy: A critical genealogy of its historical emergence and its contemporary effects. Journal of Educational Policy, 36(4), 599-620.

Gulson, D., Ahmed, S., & Baker, R. S. J.d. (2022). Artificial intelligence and formative assessment: A critical review.Learning, Culture and Social Interaction, 27, 100452.

Knox, A. (2020). The geopolitical economy of educational technology: Critical issues, trends and future directions.Journal of Educational Change, 51(4), 599-624.

Selbst, A. D., Gebru, T., Bryson, J., Reid, M., McKinney, L., Lantz, C., & Mitchell, S. (2019). Fairness considerations for ethical AI in software engineering. In Proceedings of the 2019 ACM Conference on Fairness, Accountability, and Transparency (pp. 89-97).

Wyatt-Smith, C., Lingard, B., & Heck, R. (2021). Education policy and datafication: A critical review of the literature.Journal of Educational Change, 52(2), 229-253.