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1. Experiments with a Socratic Intelligent Tutoring System for Source Code Understanding

   Alshaikh, Zeyad; Tamang, Lasang Jimba; Rus, Vasile (April 2020, The Thirty-Third International Florida Artificial Intelligence Research Society Conference (FLAIRS-32))

   Computer Science (CS) education is critical in todays world, and introductory programming courses are considered extremely difficult and frustrating, often considered a major stumbling block for students willing to pursue computer programming related careers. In this paper, we describe the design of Socratic Tutor, an Intelligent Tutoring System that can help novice programmers to better understand programming concepts. The system was inspired by the Socratic method of teaching in which the main goal is to ask a set of guiding questions about key concepts and major steps or segments of complete code examples. To evaluate the Socratic Tutor, we conducted a pilot study with 34 computer science students and the results are promising in terms of learning gains. 

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2. A Socratic Tutor for Source Code Comprehension

   https://doi.org/10.1007/978-3-030-52240-7_3  

   Alshaikh, Z. Tamang; and Rus, V. (July 2020, Proceedings of the 21stInternational Conference on Artificial Intelligence in Education)

   Reported here are the findings of a comparative study on the effects of using a Socratic Intelligent Tutoring System for source code comprehension and learning computer programming. The result shows there are significant differences between the two groups where students who used Socratic Tutor ITS improved their knowledge by 45% in term of learning gain, developed a better understanding of concepts such as nested if-else and for loop, and improved their confidence level by 13%. Furthermore, the result of the Pearson product-moment correlation coefficient shows a positive correlation (r = 0.68) between feedback from the ITS and learning gain. 

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3. Domain Model Discovery from Textbooks for Computer Programming Intelligent Tutors

   https://doi.org/https://doi.org/10.32473/flairs.v34i1.128561  

   Banjade, R. (January 2021, he International FLAIRS Conference Proceedings, 34.)

   null (Ed.)

   We present a novel approach to intro-to-programming domain model discovery from textbooks using an over-generation and ranking strategy. We first extract candidate key phrases from each chapter in a Computer Science textbook focusing on intro-to-programming and then rank those concepts according to a number of metrics such as the standard tf-idf weight used in information retrieval and metrics produced by other text ranking algorithms. Specifically, we conduct our work in the context of developing an intelligent tutoring system for source code comprehension for which a specification of the key programming concepts is needed - the system monitors students' performance on those concepts and scaffolds their learning process until they show mastery of the concepts. Our experiments with programming concept instruction from Java textbooks indicate that the statistical methods such as KP Miner method are quite competitive compared to other more sophisticated methods. Automated discovery of domain models will lead to more scalable Intelligent Tutoring Systems (ITSs) across topics and domains, which is a major challenge that needs to be addressed if ITSs are to be widely used by millions of learners across many domains. 

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4. Leveraging LLM Tutoring Systems for Non-Native English Speakers in Introductory CS Courses

   Villegas_Molina, Ismael; Nikitza_Montalvo, Audria; Ochoa, Benjamin; Denny, Paul; Porter, Leo (June 2025, 2025 ASEE Annual Conference & Exposition)

   Computer science has historically presented barriers for non-native English speaking (NNES) students, often due to language and terminology challenges. With the rise of large language models (LLMs), there is potential to leverage this technology to support NNES students more effectively. Recent implementations of LLMs as tutors in classrooms have shown promising results. In this study, we deployed an LLM tutor in an accelerated introductory computing course to evaluate its effectiveness specifically for NNES students. Key insights for LLM tutor use are as follows: NNES students signed up for the LLM tutor at a similar rate to native English speakers (NES); NNES students used the system at a lower rate than NES students---to a small effect; NNES students asked significantly more questions in languages other than English compared to NES students, with many of the questions being multilingual by incorporating English programming keywords. Results for views of the LLM tutor are as follows: both NNES and NES students appreciated the LLM tutor for its accessibility, conversational style, and the guardrails put in place to guide users to answers rather than directly providing solutions; NNES students highlighted its approachability as they did not need to communicate in perfect English; NNES students rated help-seeking preferences of online resources higher than NES students; Many NNES students were unfamiliar with computing terminology in their native languages. These results suggest that LLM tutors can be a valuable resource for NNES students in computing, providing tailored support that enhances their learning experience and overcomes language barriers. 

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5. Long Term Retention of Programming Concepts Learned Using Tracing Versus Debugging Tutors

   https://doi.org/10.1007/978-3-030-78270-2_39  

   Kumar, Amruth N. (June 2021, Artificial Intelligence in Education)

   null (Ed.)

   We studied long-term retention of the concepts that introductory programming students learned using two software tutors on tracing the behavior of functions and debugging functions. Whereas the concepts covered by the tutor on the behavior of functions were interdependent, the concepts covered by debugging tutor were independent. We analyzed the data of the students who had used the tutors more than once, hours to weeks apart. Our objective was to find whether students retained what they had learned during the first session till the second session. We found that the more the problems students solved during the first session, the greater the retention. Knowledge and retention varied between debugging and behavior tutors, even though they both dealt with functions, possibly because de-bugging tutor covered independent concepts whereas behavior tutor covered interdependent concepts. 

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