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As digital education becomes more prevalent, artificial intelligence (AI) technologies are being integrated into learning platforms, offering both opportunities and challenges. This overview explores the evolving landscape of AI in digital education, with a focus on the strengths and issues it brings to both teachers and learners.

Strengths of AI in Digital Education

Adaptive Learning. Adaptive learning is fast-spreading educational technology, pointed to creating an effective personalized teaching experience for every student. This aim is usually addressed by building customized resources and activities that address each individual’s unique learning needs. An example of such resources are AI-driven learning platforms that can analyse students’ strengths and weaknesses, learning paces, and preferences. Using these platforms allows learners to progress at their own pace, focusing on areas where they need most help.

Immediate Feedback. Whilst integrating adaptive learning can be a long process, AI technology can enhance more traditional education methods, such as grading. AI provides feedback in real-time, which helps learners identify areas of improvement and adjust their learning accordingly. Automated essay scoring (AES) is an example of a system that provides grading and scoring of written texts. Importantly, it allows teachers to assign longer writing assignments and ensures that students are given timely feedback – something that might not be possible without the AI-operated program.

Enhanced Teacher Support. Although AI can clearly benefit the students, it can also provide significant support to the teachers as well. A study of Estonian teachers, for example, proposed that teachers view AI technology positively and perceive it as supportive in retrieving learning materials, organizing content and scheduling of lessons, and reviewing homework assignments.

Issues and Concerns

Privacy and Data Security. Whilst the above-mentioned examples show that AI can provide manifold benefits to the field of digital education, the increased use of AI raises concerns about data privacy and security. Although AI programs are designed to include protective measures, many individuals give their consent without considering the extent of the information they are sharing, such as racial identity or biographical data. In relation, experts have raised an ethical issue of forcing students and their parents to use AI algorithms. Students and parents really have no choice if AI-systems are required by their schools, making their consent irrelevant.

Bias in Algorithms. Further, AI systems can inadvertently perpetuate bias present in their training data. If AI algorithms have been trained on past data that reflects existing inequalities, it is possible that they will favour certain student groups, potentially disadvantaging others. For instance, England’s A-level and GCSE secondary level examinations were cancelled due to the Covid-19 in the summer of 2020. The grade standardization algorithm was produced to determine the qualification grades of students. As the algorithm was based on schools’ previous examination results, the score distribution favored students from private schools, whilst students from minority groups were hit the hardest.


The integration of artificial intelligence into digital education offers immense promise, with strengths such as personalized learning and enhanced teacher support. However, the adoption of AI in education also presents significant challenges, including privacy and biased algorithms. Therefore, stakeholders must strike a balance between innovation and provision of courses to the users of AI programs, and safeguarding the well-being and privacy of students and educators.

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