## 6 Concluding Remarks  
While eLearning for programming languages offers benefits such as accessibility, temporal flexibility, and content reusability, it also exposes limitations of delivering a pre-fixed one-size-fits-all content to all students, engendering high costs for developing course contents and managing eLearning sessions including grading student submissions.  
Our research was motivated by the need to construct an eLearning system capable of delivering personalized, student-centric content, automating the generation of coursework content for all programming languages, and eliminating the need for instructor involvement in the management of eLearning sessions.  
Our strategy and software methodology was to build the system by applying a suite of advanced software technologies. (1) We utilize GPT to dynamically generate all course components, (2) apply GPT prompt engineering to personalize course content for each individual student, and (3) apply the closed loop of autonomous computing to automate eLearning sessions.  
The research results encompass the design of an eLearning framework covering all programming languages, a fully functional Python-based implementation, seamless integration with ChatGPT for dynamic content generation, a high degree of content personalization, and the elimination of manual effort required for managing eLearning sessions.  
The PoC implementation of this eLearning system was deployed on Amazon AWS for experiments. The experiment was conducted with faculty members, students, and developers and demonstrated a learning effectiveness of 91%. Moreover, the effort required to operate eLearning for programming languages was reduced by 99.8%.  
The usefulness of our proposed eLearning system may be summarized as follows:  
* GPT models can generate comprehensive course content for programming languages. The dynamically generated course contents for a language are comparable to the course contents created by a highly professional university instructor with years of experience in teaching programming languages.
* The content generated by GPT is highly responsive to the prompts provided by client applications. Consequently, crafting precise GPT prompts plays a crucial role in personalizing course content.
* The control loop inherent in autonomous computing finds practical application in automating the administration of eLearning systems. By seamlessly integrating autonomous control with GPT's grading capabilities for student exercise submissions, it becomes feasible to entirely replace the need for human instructors.
* A single development of this eLearning system can be used to teach an array of programming languages. This is in contrast to the conventional eLearning system where instructors have to create course content for every programming language.  
The only limitation we observed at this time was the occasional latency ranging from 3 to 8 s in the generation of course content and the grading of student submissions. The latency is attributable to the network-based interaction with the GPT server, and the processing time incurred during peak usage of the GPT server.  
Through this research, we observe several ethical issues in using GPT in education. These include the potential for bias and fairness concerns, as GPT models inherit and amplify biases present in their training data. Accountability is essential, with a need for clear guidelines on who is responsible for evaluating submitted coding exercises. Additionally, there is a risk of over-dependence on GPT technology, which might impact the development of critical thinking and creativity of programming language instructors.  
We believe that our personalized eLearning approach for programming sets the stage for configuring cost-effective, personalized programming education. More specifically, we propose to utilize this eLearning system in programming classes at the college level. The system can also be used in various existing eLearning systems of programming languages for the public.