The ethics of tech in education

Technology is advancing at a pace unimaginable even a decade ago, transforming industries and reshaping the way we live, work and learn. But such rapid innovation also brings ethical dilemmas – and nowhere is this tension more palpable than in education, where the adoption of cutting-edge technology is influencing not just how students learn but also who gets to learn and under what conditions. 

Mo Rehman, Head of the School of STEM at Arden University, discusses how failure to integrate ethical considerations into educational technology could lead us down a precarious path. 

What are the ethical risks?

• AI bias

Artificial intelligence (AI) has revolutionised education, bringing tools like predictive analytics and personalised learning experiences into classrooms – but the technology is only as unbiased as the data it’s trained on. For example, if developers feed an algorithm data that reflects existing societal stereotypes or inequities, the AI will perpetuate those biases. This can have significant implications depending on the context.

Take AI-driven grading tools, for example, designed to provide objective assessments of student performance. Research has shown these algorithms can be skewed by the data they are trained on, potentially disadvantaging students from underrepresented backgrounds. If left unaddressed, such biases risk deepening systemic inequities, rather than narrowing them. For example, students who use non-standard language patterns, such as those for whom English is a second language or those from different cultural backgrounds, may receive lower grades unjustly. This could discourage these students from pursuing academic opportunities or lead to a loss of confidence in their abilities. 

Over time, the reliance on such biased systems risks entrenching inequities in educational outcomes, further marginalising already underrepresented student populations.

• Loss of human insight 

AI systems may also fall short when it comes to understanding the subtleties and complexities of human experiences, making them ill-suited to decisions that require empathy, discretion or broader context. For example, automated grading systems might fail to recognise instances where a student’s errors stem from undiagnosed learning disabilities or mental health challenges. Such systems operate using fixed parameters and are therefore unable to consider personal circumstances or provide the nuanced feedback an educator might offer to support a student’s growth. 

Similarly, AI may not detect when a learner is grappling with broader issues affecting their studies, such as personal trauma or socio-economic challenges, leading to a lack of appropriate intervention. Over-reliance on these systems can also erode educators’ agency, as they may start deferring to AI-driven recommendations, rather than using their own judgement and experience. This shift risks dehumanising education, as important decisions are reduced to algorithmic outputs rather than thoughtful consideration tailored to individual needs. When implemented without caution, such practices may lead to an educational environment that prioritises efficiency over equity and understanding.

• Data and privacy 

AI systems often rely on vast amounts of student data—from academic records to behavioural patterns—to function effectively. However, this dependence raises significant concerns about the ethical handling of such sensitive information. Data misuse, breaches or excessive surveillance could have lasting impacts on students’ privacy and trust.

• The digital divide

AI tools have the potential to significantly widen the existing gap between students with access to technology and those without. Wealthier schools, with robust funding and resources, are often able to implement advanced AI-driven systems that personalise learning experiences, provide real-time feedback and enhance classroom efficiency. These advancements allow students in such schools to gain a competitive edge, preparing them better for future academic and career opportunities. 

Conversely, underfunded schools frequently lack the infrastructure and financial capabilities to adopt these cutting-edge tools, leaving their students at a disadvantage. This disparity perpetuates educational inequity, as students in resource-limited environments miss out on the benefits of AI-enhanced learning. Additionally, students with limited digital literacy may face further challenges when engaging with AI-based systems. Without proper training and support, these students may struggle to fully use such tools, thereby widening the knowledge and skill gap within classrooms and communities.

Building an ethical framework

The risks outlined above are not insurmountable. By embedding ethical oversight into the development and implementation of educational technologies, we can steer innovation in a direction that aligns with broader societal goals.

One way to ensure ethical considerations remain central to edtech innovations is by fostering a closer collaboration between educators and developers. Teachers, administrators and students understand the unique challenges within the education ecosystem and can provide valuable insights into how technology might address these needs without compromising ethical standards.

The onus is also on AI developers, who must also prioritise transparency in their algorithms and actively work to mitigate biases. This includes using diverse datasets for training AI systems, regularly reviewing the outputs for unintended consequences and involving external ethical review boards. Tools that consider diverse learning styles, cultural contexts and accessibility considerations will not only address equity issues but also result in widespread adoption.

Educational institutions must also adopt stringent data privacy policies that clearly define what data is collected, how it’s used, who has access to it, and when it will be deleted. Regulatory frameworks, such as GDPR, already set a strong precedent for data protection, and educational institutions should view compliance as the bare minimum, rather than the gold standard.

Policy also has a crucial role to play in shaping the ethical landscape of edtech. Governments and regulatory bodies need to stay ahead of technological developments, ensuring laws and regulations keep pace with innovation. By setting clear ethical guidelines for educational technology, policymakers can drive accountability and ensure students’ welfare remains a priority. 

This framework should mandate policies that ensure equal access to technology for all students, regardless of their socioeconomic background. Governments, educational institutions and private organisations must collaborate to provide funding and resources for disadvantaged schools, ensuring they’re equipped with modern tools and infrastructure. Additionally, ongoing digital literacy programmes should be developed for both students and educators, enabling them to use AI tools effectively and responsibly. 

The promise of educational technology is undeniable, but so are its risks. Educators, policymakers and developers must come together to ensure innovations serve as tools for empowerment, not exploitation. Without deliberate action, the sector risks deepening inequities, compromising privacy and exacerbating environmental harm.