Published by Joan Oladunjoye on the 26th October 2024
At the edge of a bustling city, amidst the noise of honking cars and the ever-present hum of digital activity, Layla Park finished her cup of tea and set it down next to her laptop. It wasn’t just another workday; it was the day her team would reveal the culmination of years of effort: an AI-driven learning platform designed specifically for K12 computer science education. Layla wasn’t just proud of the project; she was determined to ensure that it set a new standard for ethical technology integration in schools.
The year was 2030, and AI had already become a transformative force in education, personalizing lessons for individual students, predicting learning outcomes, and even suggesting tailored pathways based on a student’s learning style. Yet, Layla knew all too well the risks associated with this rapid evolution. As her fingers hovered over the keyboard, she reflected on the path that had brought her to this pivotal moment. Layla had always been fascinated by the power of AI to transform learning, but she also knew that with great power came great responsibility. In 2030, AI was ubiquitous in classrooms, but not all its effects were positive. Poorly designed AI systems had alienated students, compromised their privacy, and even contributed to environmental damage through the sheer amount of energy required to power the necessary data centers.
Her goal was different. She wanted to create an AI system that did more than just spit out personalized lesson plans. Layla envisioned a system that empowered students to think critically, question the systems around them, and build real-world problem-solving skills. As she fine-tuned the final details of the platform, Layla remembered the words of Díaz and Nussbaum (2024), who had inspired much of her team’s work. The Pedagogical Centered AI (PCAI) framework they developed emphasized that AI should always serve human teachers and learners, not the other way around. Layla’s platform would put control in the hands of educators, allowing them to guide AI-driven lessons without letting the technology overshadow their role in the classroom.
The challenge, however, was enormous. One of the biggest hurdles Layla faced was ensuring that AI didn’t just perpetuate existing educational inequalities. Bozkurt et al. (2023) had warned of this, showing how AI could further entrench the gap between well-funded schools and those in underprivileged areas. AI required not just devices but high-quality internet access, and both were luxuries many students lacked. Layla knew that her platform had to be accessible to all students, regardless of where they lived or what resources they had. To meet this challenge, her team worked tirelessly to design AI tools that could run smoothly on older devices and adapt to lower bandwidth settings. It was a logistical nightmare at times, but it was a critical step in ensuring that students in rural areas or underserved urban communities could have the same quality of learning experience as their peers in better-connected schools. Equality of access was a non-negotiable goal for Layla and her team.
The inequalities in AI’s reach were not the only concern on Layla’s mind. Environmental sustainability was another pressing issue. The proliferation of AI-driven educational platforms had led to a skyrocketing demand for data storage and processing, which consumed vast amounts of energy. As Selwyn (2021) had pointed out in a landmark study, the energy demands of AI technologies, especially those reliant on constant data collection and processing, were threatening to overwhelm global energy supplies and contribute to climate change. Layla was acutely aware of these risks. Early in the design process, she had made it clear to her team that they needed to focus on reducing the platform’s carbon footprint. Through rigorous testing and refinement, they managed to develop algorithms that were more energy-efficient than conventional models, minimizing the platform’s environmental impact. It wasn’t a perfect solution, but Layla believed it was a significant step in the right direction.
But there was another ethical dilemma Layla had wrestled with from the beginning: privacy. As schools increasingly relied on AI to monitor student behaviour, track performance, and predict future outcomes, Layla was deeply concerned about the potential for misuse. While these tools offered valuable insights to teachers, they also posed significant risks if used irresponsibly. Selwyn et al. (2020) had raised alarms about schools turning into surveillance spaces, where students felt as though they were constantly being watched. Layla was determined that her platform wouldn’t contribute to that dystopian vision. Instead, her team focused on creating an AI system that respected students’ privacy while still providing actionable insights to educators. Data collection would be minimized, and any information gathered would be anonymized wherever possible, ensuring that students felt safe and trusted in their learning environment.
Teachers, Layla knew, would play a pivotal role in ensuring that AI didn’t undermine student autonomy. The professional development of educators was essential to making sure that AI was used not just as a crutch, but as a tool that enriched the learning experience. Sun et al. (2022) emphasized the importance of equipping teachers with the necessary skills to navigate AI-driven tools, fostering a culture where educators could confidently guide their students through AI-enhanced lessons without feeling displaced by the technology. Layla’s platform integrated training modules that would allow teachers to become active facilitators of the technology, rather than passive users. These modules were designed not only to familiarize teachers with the platform but also to inspire them to use AI in ways that fostered critical thinking, creativity, and collaboration.
As the platform neared its launch, Layla couldn’t help but feel a mix of excitement and apprehension. The future of AI in education was exhilarating, but it also needed to be tempered with caution, thoughtfulness, and humanity. In her vision for 2030 and beyond, AI wouldn’t replace teachers or automate education. Instead, it would enhance learning experiences, foster creativity, and help students become the thinkers and innovators of tomorrow; all while being mindful of ethical, societal, and environmental impacts.
Looking even further into the future, Layla imagined a world where AI didn’t just personalize learning but actively promoted inclusivity and collaboration across cultural and economic divides. As Macgilchrist et al. (2020) proposed, AI had the potential to support collective problem-solving by integrating diverse knowledge systems, creating more inclusive and collaborative learning environments. The concept of a “decolonized AI” that Roberts (2023) had advocated for was especially close to Layla’s heart. She wanted her platform to be a tool not just for learning but for promoting social justice, ensuring that technology didn’t reinforce existing biases but instead helped to dismantle them.
With a deep breath, Layla hit the final key to confirm the platform’s upload. Her team’s work wasn’t about the next flashy tech innovation; it was about building a sustainable, equitable, and ethically sound future for education. And that, she thought as she closed her laptop, was the kind of progress worth fighting for.
References
Bozkurt, A., et al. (2023). Speculative futures on ChatGPT and generative AI. Asian Journal of Distance Education, 18(1).
Díaz, B., & Nussbaum, M. (2024). Artificial intelligence for teaching and learning in schools: The need for pedagogical intelligence. Computers & Education, 105071.
Macgilchrist, F., Allert, H., & Bruch, A. (2020). Students and society in the 2020s: Three future ‘histories’ of education and technology. Learning, Media and Technology, 45(1), 76-89.
Roberts, J. S. (2023). Decolonizing AI ethics: Indigenous AI reflections. Accel.AI.
Selwyn, N. (2021). Ed-tech within limits: Anticipating educational technology in times of environmental crisis. E-Learning and Digital Media.
Selwyn, N., Pangrazio, L., Nemorin, S., & Perrotta, C. (2020). What might the school of 2030 be like? An exercise in social science fiction. Learning, Media and Technology, 45(1), 90–106.
Sun, T., Strobel, J., Kim, C., Gao, Y., & Luo, W. (2022). Enhancing K-12 teachers’ AI teaching competency: A TPACK-based professional development program. Journal of Educational Computing Research, 60(7), 1824-1845.