Overview
The “Learning to Learn (L2L)” course immersed students in the intersection of human and machine learning, with a particular emphasis on AI’s potential to create personalized, self-directed educational experiences. The curriculum integrated foundational concepts in AI, ethical reflections, and practical applications, introducing students to both human learning theories and the fundamentals of machine learning while covering distinctions between supervised and unsupervised models. Students examined the role of AI in complementing human capabilities while also exploring advanced topics like large language models and the ethics surrounding AI applications.
The course was highly experiential, encouraging students to apply AI theories in real-world contexts through micro-projects using tools like Teachable Machine and various AI content generators. A strong focus on ethics and society guided students to critically assess AI’s societal impact, discussing issues such as algorithmic bias, environmental costs, and the global implications of widespread AI adoption. Ethical audits and group discussions prompted students to look beyond AI’s technical capabilities and recognize the broader implications of AI integration in the classroom and beyond.
Outcomes
The L2L course facilitated both intellectual and practical growth. Students developed autonomy in their learning, gaining confidence in tracking their own progress and adapting their approaches to deepen their understanding of AI’s educational role. Ethical discussions and tool audits broadened students’ perspectives, fostering a critical awareness of AI’s societal effects and sparking thoughtful reflections on its ethical implications. Through hands-on projects, students also gained technical skills in an accessible, low-stakes environment. Projects like avatar creation and the exploration of GPT-based models enabled them to experiment with AI applications in depth.
A sample of student projects demonstrated the course’s wide-ranging impact. One team created an automated social media marketing tool that leveraged AI for content generation, combining natural language processing with video technology. Another group designed self-study AI tutors that provided adaptive feedback and interactive practices, employing LLMs to personalize learning experiences. Additional projects included an AI-driven writing analysis website to support writing center tutors and a poker hand practice tool that used Python and ChatGPT to offer strategic guidance.
Despite its many successes, the course also highlighted challenges. Some students encountered a cognitive load from managing multiple AI tools, suggesting a need for more streamlined instruction. Concerns about potential over-reliance on AI tools also surfaced, pointing to the risk that students might disengage from non-digital, deeper forms of learning. These insights suggest that future iterations of the course could benefit from a more gradual introduction of ethical dilemmas to sustain engagement and reduce cognitive demand. Projects within the course underscored both the strengths of AI in personalizing education and the need for human oversight in areas requiring creativity and critical judgment. Through its blend of technical training, ethical reflection, and self-directed learning, the L2L course offered students a comprehensive perspective on AI, providing valuable insights into how emerging technologies shape contemporary education.
Team
Molly Chehak
Director of Digital Learning, CNDLS
Duncan Peacock
Kartikeya Uniyal