Can artificial intelligence be used to predict and personalize students’ school experiences? In the wake of the pandemic, many schools have achieved widespread access to digital devices on a one-to-one basis. Alongside this, the analysis of students’ digital usage has expanded, often occurring discreetly, such as through features like Insights in Teams. By employing various algorithms, we can gain insights into how students navigate their educational journey. Technology giants claim that the aim is to provide teachers with a deeper understanding of students’ school experiences, ultimately enabling increased personalization of learning.
This has already been exemplified through the implementation of adaptive solutions, where software programs facilitate tasks and offer personalized feedback. Platforms like Khan Academy boast a range of adaptive programs, and other examples include Reading Progress in MS Teams, among many others.
Different subjects present distinct opportunities and challenges when it comes to incorporating digital technology. One subject that particularly stands out is mathematics, as students need to master foundational calculation skills through repetitive practice. Creating programs that facilitate math problem-solving has proven to be relatively straightforward compared to providing effective feedback on students’ written work. However, recent advancements, such as the development of ChatGPT, a functional Large Language Model (LLM), have significantly changed the landscape. Nonetheless, there is still a lack of pedagogical solutions capable of effectively addressing language improvement. Given the ongoing progress, it is only a matter of time before adaptive learning solutions extend to language subjects as well.
These programs analyze and identify students’ strengths and weaknesses, enabling personalized learning experiences that target areas where students struggle. Many teachers have reported spending less time on planning and grading as a result. Moreover, students often find these programs motivating. While they excel at reinforcing familiar concepts, their effectiveness in promoting a deep understanding of the subject matter is limited.
Learning analytics involves the collection of student data, including attendance records, grades, and digital footprints left across various platforms. Artificial intelligence plays a crucial role in analyzing these digital footprints. However, in 2023, a challenge persists due to the lack of integration among different solutions within schools. This fragmentation results in data silos that do not communicate with one another. To maximize the benefits, comprehensive solutions that bring together mathematics, reading, writing, and other subjects within a unified platform are essential. Although teachers bear the primary responsibility for interpreting students’ skills, artificial intelligence has the potential to reshape our understanding of what encompasses effective assessment by educators.
Both teachers and technology providers gain access to students’ digital footprints, which they are likely to leverage for further program development. This raises important considerations for ongoing reflection. We face challenges regarding the impact of technology on our individual learning experiences. If we rely excessively on technology without engaging in reflection, wonder, and meaningful dialogue, schools may prioritize data collection on what students are learning, rather than fostering a deeper understanding of the intricate connection between learning and identity development.
As emphasized in numerous articles, society needs to engage in extensive reflection and debate on how to effectively harness artificial intelligence in education. We think that we don’t want to end up like this chinese school:
How do you feel about learning analytics?