Learning-by-teaching with ChatGPT: The effect of teachable ChatGPT agent on programming education (2412.15226v1)

Abstract: This study investigates the potential of using ChatGPT as a teachable agent to support students' learning by teaching process, specifically in programming education. While learning by teaching is an effective pedagogical strategy for promoting active learning, traditional teachable agents have limitations, particularly in facilitating natural language dialogue. Our research explored whether ChatGPT, with its ability to engage learners in natural conversations, can support this process. The findings reveal that interacting with ChatGPT improves students' knowledge gains and programming abilities, particularly in writing readable and logically sound code. However, it had limited impact on developing learners' error-correction skills, likely because ChatGPT tends to generate correct code, reducing opportunities for students to practice debugging. Additionally, students' self-regulated learning (SRL) abilities improved, suggesting that teaching ChatGPT fosters learners' higher self-efficacy and better implementation of SRL strategies. This study discussed the role of natural dialogue in fostering socialized learning by teaching, and explored ChatGPT's specific contributions in supporting students' SRL through the learning by teaching process. Overall, the study highlights ChatGPT's potential as a teachable agent, offering insights for future research on ChatGPT-supported education.

Learning-by-Teaching with ChatGPT in Programming Education

The research paper titled "Learning-by-teaching with ChatGPT: The effect of a teachable ChatGPT agent on programming education" offers an insightful examination of how ChatGPT can be leveraged as a teachable agent to enhance the learning-by-teaching (LBT) instructional strategy, particularly within the domain of programming education. The paper investigates the efficacy of engaging with ChatGPT for educational purposes, especially in the context of learning programming concepts and practices through a teaching paradigm.

Summary of Findings

The investigation primarily focused on the interplay between ChatGPT and LBT, scrutinizing whether the conversational capabilities of ChatGPT could effectively facilitate the teaching process and thereby augment a student's learning experience. Key findings from the paper reveal several facets of this interaction:

1. Improvement in Programming Knowledge: Students who engaged with the teachable ChatGPT exhibited marked improvements in their implementation of programming concepts. The act of "teaching" ChatGPT led to better performance in writing clearer and logically structured code, significantly enhancing knowledge comprehension and retention.
2. Enhanced Self-Regulated Learning (SRL) Abilities: The research highlighted that engaging with ChatGPT as a teachable agent bolstered students' SRL abilities. Students exhibited increased self-efficacy and the effective use of self-regulation strategies, suggesting a more empowered and self-driven learning process.
3. Limitations in Error-Correction Skills Development: Despite the noted benefits, the paper found that ChatGPT's innate predisposition to generate accurate code might limit students' opportunities to develop robust error-correction skills. This is attributed to ChatGPT's tendency to minimize errors in its responses, thereby reducing students' practice in debugging scenarios.

Practical and Theoretical Implications

The practical implications of these findings are significant. By utilizing ChatGPT as a teachable agent, educational systems can provide students with an interactive learning companion capable of simulating a dialogic teaching environment, thereby fostering a deeper understanding of subject matter and enhancing metacognitive skills.

On a theoretical level, the introduction of AI-powered teachable agents like ChatGPT presents a paradigm shift in traditional educational methodologies, offering a scalable and readily available alternative to peer-based tutoring and teaching exercises. This shift acknowledges the growing role of AI in education, suggesting that future pedagogical frameworks could increasingly incorporate AI elements to enhance cognitive and metacognitive learning processes.

Future Directions

The research points to several avenues for future exploration, particularly in refining AI's role in education. Future studies could investigate optimizing the balance between ChatGPT's response accuracy and its potential to deliberately integrate errors for educational purposes. Additionally, understanding the long-term impact on learning outcomes when using AI teachable agents and exploring their applications across various domains beyond programming could yield comprehensive insights into AI-driven educational models.

Conclusion

In conclusion, the paper effectively delineates how ChatGPT, positioned as a teachable agent, stands to significantly enrich the learning-by-teaching approach within programming education. However, it also highlights the necessity for meticulously designed interactions that can intentionally exploit the full spectrum of error-correction and SRL opportunities. The insights garnered from this research pave the way for more nuanced and targeted implementations of AI in education, expanding the potential for more individualized and effective learning experiences.