Exploring the Advantages of Incorporating AI and Data-Driven Technologies in Education

I recently read about the incorporation of Artificial Intelligence (AI) in the education sector, specifically in the United Kingdom (UK). As my son’s primary school has begun using DoodleLearning, which provides digital learning programs for Maths and English, and as I am a governor at a local primary school that also utilizes educational technology, I was particularly interested in the report, “AI Barometer Part 5 — Education,” produced by the Centre for Data Ethics and Innovation (CDEI) and the Department for Digital, Culture, Media & Sport (DCMS).

The report emphasizes the advantages of AI and data utilization in education, including the lessening of administrative tasks for educators via “teacher-facing” technologies and the customization of learning paths through “learner-facing” systems. It also brings attention to the potential isks and barriers that accompany these technologies.

I wasn’t surprised by the conclusions of this report on the advantages of AI and data-driven technologies in education. My studies of Technology Enhanced Learning at the Open University had already made me aware of the main benefits of TEL.

However, I was impressed to discover that the CDEI and DCMS take the issue of AI and data use in education very seriously, and have conducted a comprehensive review of policy and academic literature, as well as assembling over 80 expert panelists from industry, civil society, and academia. State involvement is essential in emphasizing the benefits of AI and data-driven technologies in education and monitoring service providers. Additionally, a government that makes well-informed decisions about the use of AI in education could also improve the learning and teaching experience in this country.

Therefore, I have decided to write a blog about this report in two parts. In this particular blog post, I would like to provide some background on the report and the discussions around AI and data use in the UK education system. Additionally, I would like to emphasize how data-driven technologies in the education sector could address some of the challenges faced by educators, administrators, and students. In my next blog post, I will concentrate on the risks and barriers associated with AI in education and examine whether the DCMS is dedicated to finding solutions to overcome these barriers to innovation.

Background to the AI Barometer and the State of Play

The report, based on the first AI Barometer, examines three crucial UK sectors that have been heavily impacted by the COVID-19 pandemic and where data-driven technologies could potentially play a role in economic and social recovery. These sectors include (i) recruitment and workforce management, (ii) transport and logistics, and (iii) education. In this particular blog post, I want to delve into how data-driven technologies can improve education in the UK.

The report highlights the challenges educators face in providing high-quality education to diverse student needs, and how excessive workload is a significant factor in the high turnover of teachers. It also notes that the COVID-19 pandemic led to a prolonged period of remote learning across the education system, which continues to affect student support needs such as catch-up and national tutoring programs.

The ultimate goal of the report is to maximize the advantages of AI and data-driven technologies in the education sector, particularly post-Covid-19 pandemic, by addressing the risks associated with the integration of AI, as well as removing obstacles so that innovation in educational technology can improve the learning, teaching and administrative experiences of those in the education field.

The report identifies three categories of applications that could help the education sector: educator-facing systems, learner-facing applications, and system-level applications.

· ‘Educator-facing’ systems aim to ease administrative and pedagogical pressures on educators. They include predictive analytics, data on students’ attainment and behavior in educational settings, natural language processing, and automating routine tasks such as compiling and distributing learning materials.

· ‘Learner-facing’ applications offer opportunities for increased personalization in learning pathways. They include Adaptive Learning Platforms (ALPs) and Intelligent Tutoring Systems (ITSs), which draw on “educational data-mining.”

· ‘System-level’ technologies can improve decision-making capabilities for administrators and planners, such as in providing effective inspection. These applications are not as advanced as the above but the Office for Standards in Education (Ofsted) has used supervised machine learning techniques to identify schools in need of full inspection since 2018.

Benefits of AI and Data-use in Education The applications mentioned above offer many benefits to the education sector:

• They address societal challenges such as social mobility and scaling up teaching provision through the use of data-driven edtech.
• They reduce administrative burdens for teachers and help with understanding what learning approaches work best (‘metacognition’) through tailored learning pathways.
• They improve the scalability and quality of education, increasing social mobility by providing access to connected digital devices.
• They augment educators’ capabilities rather than replace whole aspects of their role, for example, tools that reduce administrative workload, help teachers find resources, or provide supplemental after-school support in helping learners access high-quality content.
• They improve the quality of education by tailoring teaching to learner needs and informing educators on how effective their methods are through personalized learning pathways.
• They improve feedback for learners while also reducing educator workload through data-driven marking systems.

In conclusion, while edtech could offer many benefits, what is crucial is whether educators, learners, and administrators are fully equipped to utilize data-driven technologies and whether risks and barriers, such as privacy and autonomy concerns and limited market understanding of educator needs, may delay or prevent realizing the benefits of AI and data-driven technologies. Lastly, if the DCMS is committed to seeking solutions to the risks AI poses and the barriers to innovation in education — my next blog will look into these questions.