The Ethics of Using AI in Education
- Brainz Magazine
- Aug 11
- 6 min read
Danisa Abiel is well known for her practical solutions to teaching and learning in the advancing fields of Science, Technology, Engineering, and Mathematics (STEM). She is the founder of International Teaching Learning Assessment Consultants and Online Schools (ITLACO). She has authored 20 editions of her newsletter, "The Educator's Diaries," on LinkedIn.
Artificial Intelligence (AI) is becoming increasingly prevalent in various sectors, including education, as described in the last edition of the Educator's Diaries. From intelligent tutoring systems and automated grading to personalised learning platforms and administrative support tools, AI is changing the way educators teach and students learn. While the integration of AI holds transformative potential, it also raises profound ethical questions. This week’s edition explores the ethical implications of AI in education, considering both its benefits and risks, and argues for a framework that ensures its responsible, equitable, and transparent use (Holmes, Bialik, & Fadel, 2019; Selwyn, 2019).
The promise of AI in education
AI has introduced several innovations in education, many of which enhance efficiency, personalisation, and accessibility. These technologies can:
1. Support personalised learning
AI can analyse student data in real time to tailor content and pace according to individual needs. Adaptive learning platforms can identify knowledge gaps and recommend resources, improving learning outcomes and engagement (Luckin et al., 2016).
2. Improve accessibility
AI-powered tools such as speech-to-text, real-time translation, and assistive technologies make learning materials accessible to students with disabilities and non-native speakers, fostering inclusivity (Holmes et al., 2019).
3. Automate administrative tasks
AI can streamline routine tasks like grading, attendance tracking, and scheduling, allowing educators to focus on pedagogy and student support (Selwyn, 2019).
4. Enable data-driven insights
By analysing trends in student performance, AI systems can help educators identify at-risk students, personalise interventions, and make evidence-based decisions (Baker & Siemens, 2014).
5. Provide scalable tutoring and support
AI-driven chatbots and virtual tutors offer on-demand help to students, expanding access to learning beyond the classroom and addressing shortages in human resources (Holmes et al., 2019).
Ethical challenges and risks of AI
Despite these benefits, the integration of AI into education brings several ethical concerns:
1. Privacy and surveillance
AI systems often require large amounts of data to function effectively. This raises concerns about how student data is collected, stored, and used. Without proper safeguards, sensitive information can be exposed or exploited, compromising student privacy and autonomy (UNESCO, 2021).
2. Algorithmic bias and discrimination
AI systems can replicate or even amplify existing biases present in training data. This can lead to unfair treatment of students based on race, gender, socio-economic background, or disability status. Biased algorithms may reinforce inequalities rather than eliminate them (Jobin, Ienca, & Vayena, 2019).
3. Erosion of human interaction
Education is not solely about content delivery; it involves mentorship, emotional support, and social learning. Over-reliance on AI may weaken these human connections, making learning more transactional and less holistic (Selwyn, 2019).
4. Academic integrity
AI tools such as text generators can enable students to plagiarise or bypass the learning process. This challenges traditional notions of authorship, effort, and assessment, and calls for new strategies to uphold academic integrity (Coeckelbergh, 2020).
5. Digital divide and inequity
Not all students or institutions globally have equal access to AI technologies. Wealthier schools in more developed countries may benefit disproportionately, widening the educational gap and reinforcing systemic inequality (Williamson & Eynon, 2020).
6. Opacity and accountability
Many AI systems function as "black boxes," with decision-making processes that are difficult to understand or challenge. When AI makes a mistake or causes harm, it is often unclear who is responsible or how to appeal the outcome (Binns, 2018). This highlights the need for human interaction to decipher the grey areas of decision-making processes.
Ethical principles for AI in education
To address these risks, the deployment of AI in education must be guided by a robust ethical framework grounded in the following principles:
1. Transparency
Students, educators, and stakeholders should be informed about how AI tools work, what data is collected, and how decisions are made. Clear documentation and explainability are crucial (UNESCO, 2021).
2. Accountability
There must be mechanisms to hold developers, institutions, and users accountable for the consequences of AI use. This includes channels for redress and audit trails for algorithmic decisions (Coeckelbergh, 2020).
3. Privacy and consent
Data collection should be minimised, anonymised where possible, and governed by strong privacy policies. Students and their guardians should give informed consent and retain control over their data (UNESCO, 2021).
4. Equity and fairness
AI systems must be designed and tested to ensure they do not disadvantage any group. Developers should actively work to identify and mitigate biases (Jobin et al., 2019).
5. Human oversight
AI should augment, not replace, human educators. Teachers should retain the authority to override AI recommendations, ensuring that technology supports rather than dictates educational outcomes (Selwyn, 2019).
6. Inclusivity
Development and deployment of AI tools should involve diverse stakeholders, including educators, students, ethicists, and community members, to ensure that a broad range of perspectives is considered (Williamson & Eynon, 2020).
Implementing ethical AI in education
Putting these principles into practice requires a multi-stakeholder approach and coordinated action at several levels:
1. Policy and regulation
Governments and educational authorities should establish clear regulations governing the use of AI in education. These should align with existing data protection laws (e.g., GDPR, FERPA) and promote ethical standards across public and private sectors (World Economic Forum, 2020).
2. Institutional guidelines
Schools and universities should develop internal policies for evaluating and implementing AI tools. This includes ethical review boards, procurement criteria, and regular audits of AI performance and impact (UNESCO, 2021).
3. Teacher training and AI literacy
Educators need training to understand the capabilities and limitations of AI tools, recognise bias, and use them responsibly. Similarly, students should be taught digital and AI literacy to use these tools critically and ethically (Holmes et al., 2019).
4. Inclusive design and development
Developers should collaborate with educators and students to design tools that are user-centred and contextually appropriate. Participatory design methods can help ensure that AI aligns with educational values and goals (Baker & Siemens, 2014). This prevents them from being used solely for monetary gain.
5. Monitoring and evaluation
AI systems should be continuously monitored for effectiveness, bias, and unintended consequences. Feedback mechanisms should be in place to adapt and improve tools over time (Williamson & Eynon, 2020).
Case studies and examples
Several real-world examples illustrate both the promise and perils of AI in education:
Predictive analytics in U.S. universities
Some institutions use AI to predict student dropout risks and intervene early. While this has improved retention rates, concerns have arisen about profiling and the potential stigmatisation of students flagged as "at-risk" (Williamson & Eynon, 2020).
AI proctoring tools
During the COVID-19 pandemic, AI-powered remote proctoring became widespread. These tools raised alarms about student surveillance, facial recognition bias, and anxiety induced by constant monitoring (UNESCO, 2021).
ChatGPT in classrooms
The use of generative AI tools like ChatGPT presents opportunities for creative writing and language practice. However, educators must also grapple with plagiarism, factual inaccuracies, and the ethical use of AI-generated content (Coeckelbergh, 2020). Teachers must cross-check facts, which is time-consuming.
Adaptive learning systems (e.g., Knewton, DreamBox)
These platforms adjust content delivery based on learner performance. While they enhance engagement for some, concerns remain about data usage, algorithmic opacity, and the narrowing of curriculum (Luckin et al., 2016).
Way forwards
As AI continues to evolve, so too must our ethical frameworks. Emerging trends such as emotional AI, virtual reality-based learning, and AI-driven career counselling present new moral dilemmas. Ongoing dialogue, interdisciplinary research, and global collaboration will be essential in shaping a future where AI supports inclusive, human-centred education (Jobin et al., 2019).
AI should not be seen as a solution in search of a problem but as a tool that, when guided by ethical principles, can enrich the educational experience. A cautious, reflective, and participatory approach will be necessary to ensure that AI serves the public good without undermining the core values of education (Selwyn, 2019).
Conclusion
The use of AI in education holds tremendous potential, from personalised learning and greater accessibility to enhanced efficiency and innovation. However, it also brings ethical challenges related to privacy, bias, inequality, and the erosion of human interaction. Addressing these issues requires a commitment to transparency, fairness, accountability, and human dignity (UNESCO, 2021).
Ultimately, the ethical use of AI in education is not just about avoiding harm; it is about actively promoting justice, equity, and human flourishing. By embedding ethical considerations into the design, implementation, and governance of AI tools, we can harness their power while preserving the integrity and humanity of education (Coeckelbergh, 2020).
Read more from Danisa Abiel
References:
Baker, R. S., & Siemens, G. (2014). Educational data mining and learning analytics. In K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (2nd ed., pp. 253–274). Cambridge University Press.
Coeckelbergh, M. (2020). AI ethics. MIT Press.
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Centre for Curriculum Redesign.
Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Polity Press.
UNESCO. (2021). Artificial intelligence and education: Guidance for policy-makers.
World Economic Forum. (2020). The future of jobs report 2020.
