Generative AI and Knowledge Mapping in Programming Education: Student Learning and Engagement

Generative AI technology is rapidly advancing and is increasingly being used to automate various processes in software development, from planning to testing. In light of these technological in-novations, programming education at universities and institutes of technology must be restruc-tured to align with software development processes using generative AI. Programming and gen-erative AI are highly compatible, and generative AI can support a wide range of tasks, including automatic code generation, refactoring, code suggestion, answering programming-related ques-tions, and test code generation. In this paper, we propose a beginner-level online programming course designed to utilize generative AI as a support system for programming education. We developed educational content and implemented it in a preliminary trial with a small group of university students. Learning logs and questionnaire responses were analyzed to evaluate the ef-fectiveness of the course. Our results indicate a high level of student satisfaction with both the course content and the use of generative AI. Additionally, students demonstrated increased awareness of the importance of verifying AI-generated output and crafting appropriate prompts. These findings suggest that the integration of generative AI and on-demand learning has strong potential to enhance programming education in higher education institutions.

Purpose Generative AI (GenAI) in education promises remarkable development changes that can enhance learner experience through the personalised and responsive learning process. In this paper, we investigate how the different features of GenAI – autonomy, interactivity and usability – contribute to student engagement and learning outcomes. Design/methodology/approach From the lens of the self-determination theory, the study seeks to articulate how these GenAI features influence student engagement and their ability to achieve specific goals through interactive learning experiences delivered by GenAI. Qualtrics, a well-known online data collection tool, was employed in the study to collect data from 488 respondents in the UK region who had been exposed to generative AI-based platforms. The sampling approach used was representative, wherein the platform was asked to randomly share the survey with respondents in the UK region who had some experience interacting with Gen AI platforms. Findings The paper’s findings show that increased autonomy and interactivity encourage students to engage more in GenAI platforms. Usability functions as a critical mediator with respect to how autonomy and interactivity are incorporated into engagement and skill acquisition. Skill development emerged as a key mediator of the direct effect between GenAI features and student engagement, confirming that it is an important determinant in engaging with educational content. Further, results reveal a significant positive moderation effect of personalisation between autonomy and engagement. Originality/value The results extend the self-determination theory and offer theoretical suggestions for improving learning through AI-driven educational tools. For educators, the study provides practical insights to motivate the effective integration of GenAI platforms that can provide autonomy, interactivity, usability and personalisation to increase student engagement.

This research focuses on the possibility of utilizing generative AI in developing learning content to suit each learner’s requirements. The study will evaluate the outcomes of the use of AI-created content in enhancing students’ interest, desire, and performance in contrast to conventional learning resources. A qualitative study involves interviewing educators and developers of AI to understand their experience and perception about the generative AI in education while the quantitative study involves the performance data of students to determine the effectiveness of the content generated by AI. The paper also explores the ethical and privacy issues that come with the integration of AI in learning and offers solutions to these issues. To compare the efficiency of the AI-generated learning material with the traditional one, the study designed a quantitative comparative study on the performance of the students in Object-Oriented Programming (OOP) course. The course was split into two equal independent assessments; the professors uploaded AI generated content such as the title of the lesson, the content that was taught, and the learning outcomes expected, for each class. LMS was integrated with the OpenAI API to write content that is in line with the learning objectives as defined earlier. Performance data of students as obtained from the two evaluations was used to determine the effect of using AI-generated contents on students’ learning. The results indicate that despite the students’ increased test scores and grades after applying AI-created study materials, some of them are not benefited from them. These are some of the effects that show that it is essential to consider aspects like students’ interest, their prior knowledge, and the quality of the AI model while adopting generative AI in education.

Generative AI (GenAI) technologies have been widely adopted by college students since the launch of ChatGPT in late 2022. While the debate about GenAI’s role in higher education continues, there is a lack of empirical evidence regarding whether and when these technologies can improve the learning experience for college students. This study utilizes data from a survey of 72,615 undergraduate students across 25 universities and colleges in China to explore the relationships between GenAI use and student learning engagement in different learning environments. The findings reveal that over sixty percent of Chinese college students use GenAI technologies in Academic Year 2023–2024, with academic use exceeding daily use. GenAI use in academic tasks is related to more cognitive and emotional engagement, though it may also reduce active learning activities and learning motivation. Furthermore, this study highlights that the role of GenAI varies across learning environments. The positive associations of GenAI and student engagement are most prominent for students in “high-challenge and high-support” learning contexts, while GenAI use is mostly negatively associated with student engagement in “low-challenge, high-support” courses. These findings suggest that while GenAI plays a valuable role in the learning process for college students, its effectiveness is fundamentally conditioned by the instructional design of human teachers.

Introductory programming courses are considered difficult and challenging for students. They have to focus on and develop different skills related to problem-solving and programming domains concurrently. However, most programming courses spend more time teaching programming syntax. Therefore, this study developed and introduced an application, OOP-SOLVE, which focused on algorithmic thinking skills in the object-oriented programming (OOP) domain. The pseudo-code technique is used to create this application. Most of the teaching topics of the OOP course, such as classes, objects, constructors, inheritance, and polymorphism, are covered in this application. Moreover, the application presents each programming question in different sections such as class diagram, main class, test class, execution process, and output. A technology acceptance model (TAM) was used to investigate the acceptance of the OOP-SOLVE application in the OOP course. Moreover, the perceptions of the OOP course lecturers regarding the OOP-SOLVE application were collected by conducting a semi-structured interview. 224 students participated in the survey, and six lecturers participated in the interviews. Results show a positive impact of perceived ease of use, usefulness, and enjoyment on students’ attitudes toward their intention to use the application in the course. Lecturers also agreed that the application supported students in the OOP course. Moreover, it promotes students’ engagement and enhances collaboration and interaction among students in class activities. In addition to the solution of the given programming statement, the OOP-SOLVE application also presents the execution process of the program along with the output of each programming question. Lecturers also agreed that the application can be a supporting teaching tool in the OOP course.

Programming Languages play a crucial role in developing any software applications. Academicians and IT professionals give more priority for mastering many programming languages. Learning many language syntax and constructs will not contribute much in building problem solving skills. For any given task, students should be capable of solving it by using any of the programming languages. More importantly, students should build an art of optimizing the performance of the application by applying fundamental and advanced concepts of programming languages. In this paper, we propose an innovative way of designing and teaching of Object Oriented Programming Concepts without sticking to one particular language. In our first attempt, we practiced teaching of this course with two languages namely, C++ and Java. Our experimental results and students feedback prove the effectiveness of designing and teaching of Language independent Object Oriented Programming course. Moreover, it develops the skills of implementing and applying object oriented concepts using any object programming language.

This pilot study examines how students’ performance has evolved in an Object-oriented (OO) programming course and contributes to the learning analytic framework for similar programming courses in university curriculum. First, we briefly introduce the research background, a novel OO teaching practice with consecutive and iterative assignments consisting of programming and testing assignments. We propose a planned quantitative method for assessing students’ gains in terms of programming performance and testing performance. Based on real data collected from students who engaged in our course, we use trend analysis to observe how students’ performance has improved over the whole semester. By using correlation analysis, we obtain some interesting findings on how students’ programming performance correlates with testing performance, which provides persuasive empirical evidence in integrating software testing practices into an Object-oriented programming curriculum. Then, we conduct an empirical study on how students’ design competencies are represented by their program code quality changes over consecutive assignments by analyzing their submitted source code in the course system and the GitLab repository. Three different kinds of profiles are found in the students’ program quality in the OO design level. The group analysis results reveal several significant differences in their programming performance and testing performance. Moreover, we conduct systematical explanations on how students’ programming skill improvement can be attributed to their object-oriented design competency. By performing principal component analysis on software statistical data, a predictive OO metrics suite for both students’ programming performance and their testing performance is proposed. The results show that these quality factors can serve as useful predictors of students’ learning performance and can provide effective feedback to the instructors in the teaching practices.

With the rapid advancement of generative AI, automation is increasingly being introduced across various stages of software development. In response to these changes, programming education must also evolve to incorporate the use of generative AI from the outset. In this study, we designed and implemented intermediate-level programming courses that integrate generative AI tools such as GitHub Copilot. The curriculum consisted of three subjects: Object-Oriented Programming, Test-Driven Development, and Practical Project Development. Each course combined on-demand instructional materials with AI-assisted exercises. As a result, learners reported high levels of satisfaction and frequently accessed course materials and assessments. Notably, many students demonstrated the ability to critically evaluate and adapt AI-generated suggestions rather than relying on them uncritically. A comparative survey between GitHub Copilot and Google Gemini revealed that students were also beginning to select AI tools based on purpose and context. These findings indicate the potential of educational designs that foster practical programming skills and cultivate AI literacy. This initiative highlights the promise of programming education that is both AI-integrated and personalized, offering new directions for curriculum innovation in higher education.

The Second International Workshop on Trends in Functional Programming in Education, TFPIE 2013, was held on May 13, 2013 at Brigham Young University in Provo, Utah, USA. The goal of TFPIE is to gather researchers, professors, teachers, and all professionals interested in functional programming in education. Submissions were vetted by the TFPIE 2013 program committee using prevailing academic standards. The 2 articles in this volume were selected for publication as the result of this process. Tobin-Hochstadt and Van Horn report on their solution to the difficult transition between the first semester course in functional programming (using languages, programming environment, etc. intended for teaching) to the second semester course in object-oriented programming (with a production-oriented language, environment, etc.). Finding that this confusing circumstance made the key concepts hard to grasp for students, the authors present and evaluate a new introduction to the second semester course, based on the environment and languages the students used before, that focusses on key object-oriented concepts. Caldwell lays out an education narrative that focusses on reasoning about programs, using structural induction principles. He argues that more such formal reasoning should get more emphasis in programming education and demonstrates the feasibility thereof by reporting on his experiences using this narrative in the functional programming course at the University of Wyoming.

This paper presents two complementary quantitative studies examining the integration of generative AI (GenAI) tools into programming courses in higher education. Study 1 investigated undergraduate students’ perceptions of GenAI tools, focusing on usefulness in coursework, creativity enhancement, behavioral intention to use, and concerns and critiques. Study 2 evaluated undergraduate students’ performance in correcting code generated by large language models (LLMs) during programming exams, comparing this performance to their results on instructor-designed programming tasks. Findings from Study 1 revealed generally favorable student perceptions of GenAI. Participants reported medium-to-high levels of perceived usefulness, particularly emphasizing GenAI’s potential to enhance learning efficiency and support creative problem-solving. Students also expressed strong intentions to continue using GenAI tools in their studies, while reported concerns were relatively low and centered primarily on the risk of over-reliance. Findings from Study 2 showed that students encountered significantly greater difficulty when correcting LLM-generated code compared to traditional exam tasks, highlighting the unique challenges posed by AI-generated outputs in assessment contexts. Taken together, the results suggest that while students recognize the value of GenAI tools in supporting learning and creative exploration, programming education must also focus on developing students’ skills in critically evaluating and correcting AI-generated content to ensure effective and responsible integration.

Research shows that feedback practices significantly impact key student outcomes, including performance, engagement, and satisfaction (Esmaeeli, Shandiz, Shojaei, Fazli & Ahmadi 2023). Feedback is a crucial component of learning in Higher Education (HE) and plays a vital role in developing critical thinking, improving retention, and enhancing student engagement. The importance of timely dialogic feedback in enhancing student engagement and potentially improving retention is well understood (Advance HE 2020). However, academic staff are increasingly time-poor, with reduced opportunities to provide regular in-depth quality feedback outside of that given for summative assessment (Henderson, Ryan & Phillips 2019). Early experimentations with using Generative AI (GenAI) such as ChatGPT to provide feedback for formative assessment recognises that students will learn and work in an AI-enabled world beyond their university studies (Bowditch 2023). GenAI can be leveraged inside and outside the classroom to achieve positive student engagement and improved skill development thereby affording them the skills and knowledge necessary to succeed (Hooda et al. 2022). Engaging with GenAI for feedback purposes offers an opportunity to increase equitable access to feedback across the student cohort, to support and further develop their critical skills and learning outcomes. As Verhoeven and Rana (2023) note, “AI disruption may present an opportunity to shift the focus from assessment of learning to assessment for learning”. Utilising GenAI for feedback purposes can provide rapid, personalised learning support, and aid with planning, drafting, and revising student work. However, this adoption of GenAI for feedback must be driven and developed by the educator, keeping the human in the loop to ensure quality (Atchley, Pannell, Wofford, Hopkins & Atchley). Our project draws on the principles of feedback literacy, current research on using AI as learning tool (Verhoeven & Rana 2023b; Tubino & Adachi 2022) and emphasises student-centred learning through dialogic feedback practices. The project draws on scholarship from Mollick and Mollick’s seven approaches to student use of AI (2023), Perkins, Furze, Roe and MacVaugh’s framework for ethical integration of AI in assessment (2024), and emerging work from Liu, Brightman, and Miller on GenAI and feedback (2023). This presentation addresses the conference theme of Technology, providing an overview and reflection on staff development and adoption of GenAI for feedback processes for the benefit of student learning. We will showcase four use cases of the use of GenAI to design and implement feedback creation for undergraduate formative assessment across the three Colleges at the University of Newcastle. All cases engage innovation in Technology Enhanced Learning (TEL) practice in developing GenAI tools to support student learning via feedback. The presentation addresses the benefits and challenges of each approach. Recognising the value of feedback in student learning, this PechaKucha is aimed at a diverse audience in HE. Our presentation will demonstrate applicability and adaptability to a range of disciplines as we explore the impact new and emerging GenAI technologies can have on HE. We will introduce the possibilities of using GenAI for feedback purposes, and encourage staff to consider experimenting with and adopting their own innovative TEL practices.

There are many unsupported claims in the media about how students use generative AI (GenAI) to support their assessment tasks, often labelling such use as inherently unethical. In response, this paper presents a semester-long investigation into 11 university students’ engagement in dialogues with GenAI for their graded assignments, using authentic chat records as the primary data. The study identified four student engagement patterns with GenAI – behavioural, (meta)cognitive, emotional and agentic. While less common, agentic engagement appeared in 12% of the dialogues where students intentionally challenged GenAI’s output and integrated their own understanding of the assessment to direct its responses. Such engagement shows that students’ GenAI use does not necessarily mean they avoid critical thinking or deep learning. We argue that promoting agentic engagement requires more than teaching students how to write better prompts but more deliberate effort to help students reflect on the purpose and process of their GenAI use.

As one mainstream of current software development, Object-oriented programming has become one key course for undergraduate students in Computer Science. Since Object-oriented concepts are difficult to understand for students, small programming exercises are used to train and help the students, and the study performance is evaluated based on the quality of the submitted source code. The common practice of code assess-ment in programming courses is checking whether the submitted projects pass carefully-designed test cases. However, even some projects pass all test cases, they may have bad software design and do not use the knowledge of Object-oriented programming well, especially in the early stage of courses. Therefore, we propose an anomaly detection approach for early warning in Object-oriented programming courses, which can automatically find the abnormal application of Object-oriented knowledge. In our approach, we conduct static analysis on the code submitted by students. Typical Objected-oriented metrics are extracted, and students are divided into two groups by K-means clustering: being good at Object-objected knowledge or not, and finally detect anomalous students based on the distance from cluster centers. We evaluate our approach on the realistic data sets collected from our Object-oriented programming course, and experimental results show the effectiveness of our method.

Despite abundant research on the pedagogical benefits of generative AI (GenAI) in ELT, teachers’ agentive use of GenAI in an instructional context has not been fully explored. How pre-service teachers utilize GenAI in teacher-training courses also remains unclear. Thus, grounded in the framework of teacher agency and professional development, this study explored pre-service English teachers’ perceptions of engaging in a GenAI-enhanced lesson design project. Thematic analysis of interviews and reflection papers from eighteen pre-service English teachers who participated in lesson design with diverse GenAI tools revealed their agentive use of GenAI in two aspects: (1) agentive role in teacher–GenAI collaboration; (2) responsible GenAI use to address its constraints. The findings highlight the importance of pre-service teacher agency in leveraging GenAI for ELT. Pedagogical implications for training pre-service teachers in GenAI literacy are discussed.

This research aims to develop ITSJava application as an interactive learning media for object-oriented programming (OOP) course in information technology education programme at Gorontalo State University. ITSJava was developed using the Personal Xtreme Programming (PXP) development method which consists of six stages: needs identification, planning, initial iteration, design, implementation, and system testing. The results of system testing show that the ITSJava application is suitable for use as a medium for learning OOP. This system can help students learn OOP in an interactive and adaptive way to students' learning styles. Hopefully, this system can be further developed by adding a unit test function to ensure the accuracy of the program code written by students and ensure that the code runs according to the expectations and logic expected by the lecturer.