Balancing AI Tools and Traditional Learning: Integrating Large Language Models in Programming Education

The University of Michigan study examined several specific social factors in relation to the adoption and use of Large Language Models (LLMs) in programming courses1. These factors included:

1. Social Perceptions: How students perceive the use of LLMs and their impact on their learning process and career prospects1.
2. Peer Influence: The effect of students' perception of their peers' usage of LLMs on their own adoption and usage1.
3. Career Expectations: How students' career aspirations and expectations of future use of LLMs in their careers influenced their decision to use these tools during their course.

The study found that these social factors significantly influenced students' adoption and use of LLMs. For instance, students who believed that over-reliance on LLMs would hurt their job prospects tended to prefer learning programming skills independently. On the other hand, those who anticipated a high future use of LLMs in their careers were more likely to engage with these tools during the course. The study also found that the perceived over-reliance on LLMs, rather than their actual usage, was associated with decreased self-efficacy later in the course.

Large language models (LLMs) like OpenAI's GPT have the potential to significantly impact the teaching and learning processes in undergraduate programming courses. These models can generate, debug, and explain code, offering new ways to assist students in their learning journey. However, their impact on students' learning processes, self-efficacy, and career perceptions is still being studied.

A study conducted by researchers from the University of Michigan found that students' use of LLMs was associated with their future career expectations and perceptions of peer usage. The study revealed that early LLM usage correlated with lower self-efficacy and midterm scores. However, the perceived over-reliance on LLMs, rather than their actual usage, is associated with decreased self-efficacy later in the course.

The integration of LLMs into the curriculum showed mixed outcomes in students' programming self-efficacy and learning achievements. Some students found that using LLMs helped them understand complex coding concepts and error messages, while others felt that it negatively impacted their confidence in their coding abilities.

In conclusion, while LLMs can significantly enhance learning experiences, over-reliance on these tools can negatively impact students' confidence and performance. Therefore, finding a balance in using LLMs is crucial to ensure students build strong foundational skills while leveraging AI tools for enhancement.