Embracing large language models in education: insights and reflections from the Caribbean context

The integration of artificial intelligence (AI) in education has opened new pathways for enhancing teaching and learning, particularly in the field of computer science. The advent of Large Language Models (LLMs) has revolutionized various domains, including software development and education. LLMs, such as OpenAI's Codex, are the underlying technology behind applications such as GitHub Copilot, which can generate code, suggest solutions, and provide real-time assistance to learners directly within integrated development environments (IDEs) (Vadaparty, Junaid, & Kumar, 2024; Porter & Zingaro, 2024). Consequently, they are transforming the teaching, learning, and practicing of coding, particularly in introductory programming courses.

GitHub Copilot, a tool developed by GitHub in collaboration with OpenAI, has garnered significant attention for its role as a ‘pair-programming bot,’ assisting students with code generation, debugging, and syntax explanations (Porter & Zingaro, 2024; Vadaparty et al., 2024). Nevertheless, despite the considerable advantages these tools offer, they also pose pedagogical and ethical dilemmas, including concerns about overdependence on AI (Johnson, 2023), and disengagement from cognitive processes (Jones, Smith, & Taylor, 2023). These contrasting aspects warrant careful examination, particularly in various educational contexts that have traditionally been underrepresented in technology-focused educational research (Brown & Adams, 2023). As such, there is a need for empirical investigations that explore how student in diverse global and regional settings interact with AI programming tools and how these tools influence their learning trajectories.

In recent times, educators and researchers have begun investigating methods to incorporate Large Language Models (LLMs) into computer science education to aid students in developing programming abilities (Chen, Zhu, & Díaz del Castillo, 2023). These investigations reflect a broader shift toward leveraging artificial intelligence to enhance learning environments and equip students with tools that mirror real-world coding practices. As AI continues to shape the digital landscape, it is imperative that its application in education be rigorously examined to ensure pedagogical value and effectiveness.

Tools powered by LLMs have shown promise in enhancing productivity, offering contextual feedback, and providing platforms for real-time exploration of complex concepts (Jošt, Taneski, & Karakatič, 2024; Wermelinger, 2023a, b). These tools offer students immediate support that can bridge gaps in understanding and scaffold their problem-solving efforts. However, the literature also cautions against the indiscriminate adoption of such technologies. Scholars have noted that overreliance on AI-generated suggestions may lead to reduced creativity, lower cognitive engagement, and ethical concerns regarding the authorship and originality of the code produced (Denny, Kumar, & Giacaman, 2023). Therefore, thoughtful integration and critical engagement are essential to maximize benefits while minimizing unintended consequences.

Although global interest in LLMs in education is growing, there remains a significant gap in research addressing how students from the Caribbean region, are interacting with these technologies. Educational systems in the Caribbean may differ from those in North America and Europe in terms of curriculum structure, digital infrastructure, and pedagogical approaches. As Miller (2021) and Thompson and Lee (2020) suggest, understanding the influence of LLMs in these distinct contexts is crucial for ensuring equitable, context-sensitive applications of AI in education. Addressing this gap in the literature is vital to advancing a more inclusive and globally relevant discourse on AI-supported learning.

The importance of this research arises from the growing role of AI in education and the urgent need for localized insights into its effects. As AI tools become commonplace in software development, it is vital that students not only gain proficiency in their use but also cultivate the ethical and critical thinking abilities necessary for responsible engagement with such technologies (Gonzalez, 2021). This is especially relevant in under-researched educational contexts, where the adoption of AI may outpace institutional readiness to address its implications.

In the Caribbean, where educational digital transformation is underway, this study aims to examine how students are adjusting to AI-enhanced learning and to recognize the essential support systems required for meaningful interaction with large language models (LLMs). Gaining an understanding of these factors is essential for informing pedagogical approaches and policy-making in the region that support equitable and meaningful integration of emerging technologies.

This study investigates how Caribbean undergraduate students engage with educational tools powered by large language models (LLMs), with a specific focus on GitHub Copilot. Although existing research has explored the use of LLMs in computing education (e.g. Lyu, Wang, Chung, Sun, & Zhang, 2024; Raihan, Siddiq, Santos, & Zampieri, 2025), much of the literature remains rooted in Global North contexts and fails to capture the unique challenges faced by students in developing regions. These challenges include inconsistent access to high-speed internet, limited opportunities for hands-on programming, and culturally specific attitudes toward technology.

In this context, educators are tasked with integrating advanced AI tools into their pedagogy while upholding academic integrity and fostering meaningful learning. It is therefore imperative to understand not only the practical difficulties students face when using tools like GitHub Copilot but also the ways in which such tools shape students' attitudes, learning behaviors, and ethical decision-making. This research addresses a critical gap by offering empirical insights grounded in a Caribbean education setting, thereby contributing to a more inclusive and context-sensitive understanding of AI in education.

This research was conducted to investigate the experiences of first-year undergraduate students at a Caribbean university regarding the use of GitHub Copilot in an introductory programming course. The study aims to achieve the following objectives:

1. Examine the technical challenges associated with implementing GitHub Copilot in educational settings.

2. Investigate students' ethical issues, such as their views on plagiarism and its effects on independent thought.

3. Assess students' attitudes toward GitHub Copilot as a learning tool.

4. Provide evidence-based findings to support educators in creating inclusive, ethical, and effective learning experiences that incorporate large language models (LLMs).

This research is of great importance as it addresses a significant gap in the existing literature by providing insights from a Caribbean perspective, which is often overlooked in global conversations about educational technology. By highlighting the experiences of students in this area, the study enhances global comprehension of the impact of AI on learning. Moreover, the study provides actionable implications for educators and institutions seeking to integrate large language models (LLMs) into their teaching programs. The results can directly influence instructional methods, curriculum development, and the creation of institutional policies that promote ethical practices and support student agency. Ultimately, the study contributes to the ongoing discourse on how LLMs are not only transforming the acquisition of technical skills but also reshaping the nature of cognitive engagement, creativity, and problem-solving in programming education. It lays a solid foundation for future research on AI-human collaboration in educational settings and supports the creation of inclusive digital learning ecosystems.

Three primary research questions (RQ) guided the study:

The three research questions posed in this study are critical for understanding the multifaceted implications of integrating GitHub Copilot into programming education within a Caribbean context. The first question addresses the technical challenges students face, recognizing the importance of evaluating how AI-generated code impacts learners' comprehension, debugging skills, and programming fluency. As the tool may offer advanced or inaccurate code suggestions, understanding these challenges is necessary to support effective integration.

The second question, exploring ethical concerns, reflects the growing discourse on plagiarism, dependency, and the erosion of independent thinking linked to AI-assisted learning. Investigating students' awareness and concerns about ethical use is crucial for informing institutional policy and promoting academic integrity. Lastly, the third question, focusing on students' sentiments, offers insights into learners' emotional and motivational responses to AI-supported tools, helping educators assess whether Copilot fosters or hinders engagement and confidence. Together, these questions align with the study's objectives and provide a holistic view of both the opportunities and tensions AI introduces into programming pedagogy, particularly within underrepresented educational contexts like the Caribbean.

The integration of artificial intelligence (AI) tools such as GitHub Copilot into programming education has sparked widespread discussion around their pedagogical value, technical reliability, and ethical implications (Gonzalez, Smith, & Wang, 2022). As large language models (LLMs) become more prevalent in software development and education, educators are grappling with how best to incorporate these tools without compromising foundational learning objectives (Krause & Elliot, 2023). This review explores the existing literature on the technical, cognitive, and security-related challenges associated with GitHub Copilot in educational contexts. It also examines how these challenges affect student engagement, learning outcomes, and instructional practices, particularly in introductory programming courses (McCarthy, Johnson, & Lee, 2023). Understanding these dynamics is essential for developing informed, ethical, and effective strategies for integrating LLMs into computer science education (Miller & O'Reilly, 2022).

Integrating GitHub Copilot into programming instruction presents several technical challenges, particularly due to its reliance on natural language processing and its limitations in managing complex programming tasks (Fowler, 2022). While the tool demonstrates the potential to enhance coding efficiency by translating natural language descriptions into code, students often encounter issues related to the robustness, correctness, and security of its outputs (Zhou & Li, 2023). These challenges underscore the importance of developing critical evaluation skills among students and adapting teaching strategies to maximize Copilot's benefits while mitigating its limitations.

A key concern in educational contexts is the inconsistency of Copilot's responses to semantically equivalent prompts. Mastropaolo et al. (2023) observed that different natural language descriptions of the same programming task led to different code outputs approximately forty-six (46%) of the time. This variability in code suggestions can be confusing, particularly for novice programmers who may lack the experience to discern optimal solutions. Instructors must guide students to critically interpret Copilot's recommendations and understand that slight variations in input phrasing can yield inconsistent results.

The correctness of Copilot's generated code also decreases as task complexity increases. While Copilot performs adequately with basic programming exercises, its effectiveness diminishes significantly in advanced problem-solving contexts. Avramovic, Avramovic, and Wojtusiak (2024) and Mo et al. (2025) found that Copilot successfully suggested correct solutions for about seventy percent (70%) of general LeetCode problems; however, the accuracy dropped to just 43.4% for more difficult problems. These findings raise concerns about overreliance on AI-generated solutions in advanced coursework, where precision and logical structure are critical.

Another significant challenge associated with GitHub Copilot is the potential for generating insecure or vulnerable code. Since Copilot is trained on a wide range of open-source repositories, it may reproduce insecure coding patterns embedded in its training data. Pearce, Ahmad, Tan, Dolan-Gavitt, and Karri (2025) found that nearly forty percent (40%) of code generated in security-sensitive contexts contained high-risk vulnerabilities. This is particularly problematic for students who may not yet possess the skills to identify and address such flaws, reinforcing the need for strong foundational instruction in secure coding practices.

Beyond the technical limitations, GitHub Copilot introduces new pedagogical considerations. Educators must address how AI-assisted tools influence the development of core programming competencies and academic integrity. Wermelinger (2023a, b) emphasizes the need for a balanced approach that incorporates Copilot without allowing it to replace essential problem-solving and logical reasoning skills. The practice of “prompt engineering,” or refining inputs to produce better code suggestions, is emerging as a vital literacy in this new learning landscape. As Denny et al. (2023) suggest, engaging students in critical dialog around Copilot's outputs can enhance computational thinking and promote a deeper understanding of coding practices.

While GitHub Copilot holds significant promise for transforming how programming is taught and learned, its integration into educational settings must be handled with caution. The variability, correctness, and security issues inherent in AI-generated code demand a measured approach, one that equips students with the skills to critically evaluate and responsibly use these tools. Educators play a key role in shaping how Copilot is used as a supportive learning companion rather than a shortcut that bypasses the learning process.

The conceptual framing of this study is grounded in the interaction between student-facing artificial intelligence (AI) tools, specifically large language models (LLMs) like GitHub Copilot (Becker et al., 2023) and students' emotional and ethical responses to their use in an educational setting (Yan et al., 2024). Student-facing AI refers to technologies that directly support learners by offering guidance, feedback, and content generation, often simulating the role of a tutor or collaborator. In this study, GitHub Copilot served as a real-time programming assistant, offering code suggestions and explanations as students completed coursework in an introductory programming class. The analysis focuses on how students perceived and engaged with this LLM-powered tool, particularly in terms of their affective experiences (e.g. confidence, anxiety, frustration) and ethical considerations (e.g. concerns about plagiarism, overreliance, and originality).

By exploring the intersection of AI tool usage and student sentiment, this framing enables a nuanced understanding of how emerging technologies influence both the cognitive and emotional dimensions of learning. It also highlights the importance of ethical agency and reflective practice in AI-supported learning environments. This perspective is particularly relevant in underrepresented contexts, such as the Caribbean, where local infrastructural and cultural factors shape the adoption and perception of educational technologies. Figure 1 visually summarizes this framework, illustrating the relationship between student-facing AI tools, student interaction, emotional and ethical responses, and the resulting instructional implications.

This study explores the integration of GitHub Copilot into a first-year undergraduate programming course at a university in the Caribbean. The study is framed within the context of evolving skill demands in software education, where students are increasingly expected to collaborate with AI systems as part of the coding process. By examining both the technical and ethical challenges associated with the use of Copilot, this research contributes to the growing body of knowledge on AI-supported learning environments and provides much-needed insight from a regional perspective. Therefore, it is intended to guide the development of inclusive, ethical, and effective teaching strategies that embrace innovation without compromising critical thinking or academic integrity. The study examines the opportunities and limitations of integrating LLMs in programming education in a Caribbean context by analyzing student feedback and engagement patterns.

This study employed an exploratory case study design to investigate how GitHub Copilot, a large language model-based tool, impacted teaching and learning experiences in a Caribbean higher education institution. The approach was chosen to allow for an in-depth examination of the instructor's instructional adaptations and students' interactions with AI-supported programming tasks within an authentic classroom context. This design enabled the researchers to capture nuanced, context-specific insights into the pedagogical and experiential dimensions of AI integration in computer science education.

The study involved thirty-seven first-year undergraduate students enrolled in an introductory programming course. Of these, 25 were male and 12 were female. The participants were novices in programming, with minimal prior exposure to the subject. Only one student reported any previous experience, which was limited to high school-level programming and did not include university-level coursework in computing. The course was taught by a lecturer who had extensive teaching experience, having delivered this programming course for the past three years and a similar course at another campus of the same institution for an additional three years. The lecturer was also familiar with GitHub Copilot and had prior experience experimenting with large language models (LLMs) in educational settings.

To initiate the study, all participating students were instructed to create a GitHub account and subsequently apply for a GitHub Copilot student license. Upon successful registration and configuration of their development environments, students were introduced to GitHub Copilot and the underlying principles of Large Language Models (LLMs), which were integrated as core tools to support laboratory exercises within the programming course. Students were not required to utilize GitHub for version control or as a remote repository in this study. Instead, GitHub was used solely as a platform to obtain student access to GitHub Copilot. Following license activation, students enabled Copilot within their chosen code editor, specifically the open-source Integrated Development Environment (IDE) Visual Studio Code, which served as the standardized development environment for all programming tasks.

The teaching approach emphasized both the technical and ethical dimensions of AI-assisted coding. Students engaged in structured discussions on the ethical use of tools like Copilot, addressing topics such as intellectual property, authorship, reliance on AI-generated content, and the broader implications of automation in programming. Key ethical policies and responsible usage guidelines were also reviewed in depth.

During lecture examples, students were introduced to effective prompt engineering, learning how to craft clear, specific prompts to guide Copilot in generating accurate and relevant code. Demonstrations were conducted to compare outcomes from well-crafted prompts versus vague or misleading ones. This was complemented by instruction on interpreting, revising, and debugging AI-generated code to align with intended functionality and coding standards. Emphasis was also placed on the importance of writing and understanding clear documentation, both to improve Copilot's output and to support human collaboration.

To gain a deeper understanding of students' experiences with GitHub Copilot, a mixed qualitative-quantitative analytical approach was employed. This section outlines the two primary methods used to analyze the students' written reflections: content analysis and sentiment analysis. These complementary techniques enabled a systematic examination of both the substance and tone of the students' feedback, aligning with the study's three research questions.

To address the research questions regarding the technical (RQ1) and ethical (RQ2) challenges students faced when using GitHub Copilot, a qualitative content analysis was conducted on students' written reflections. Throughout the twelve-week academic term, students engaged with GitHub Copilot as part of their regular programming assignments. At the end of the course, they were prompted to respond to an open-ended reflection question designed to elicit their experiences with the tool. These reflections offered rich insight into the challenges encountered and students' evolving attitudes toward the role of AI in software development.

The open-ended responses were compiled into a textual dataset and subjected to inductive content analysis. The initial phase involved familiarization with the data, followed by open coding to identify recurring themes related to technical difficulties, such as incorrect code suggestions or integration issues and ethical concerns, including overreliance, originality, and academic integrity. Codes were then organized into broader thematic categories aligned with the two research questions. In addition, a tone analysis was conducted to classify the emotional valence of the responses as positive, negative, or neutral, providing further context to the students' experiences. This methodological approach enabled a nuanced understanding of the specific students' challenges encountered and their reflections on the implications of using AI-assisted programming tools in an educational setting. A visual summary of this process is presented in Figure 2, which illustrates the sequential steps of data collection and analysis used to derive insights from students' reflections.

To answer the third research question, sentiment analysis was employed to computationally determine the polarity of students' responses. Specifically, we utilized the VADER (Valence Aware Dictionary and sentiment Reasoner) Sentiment Intensity Analyzer python module, a lexicon and rule-based model optimized for social media text but proven effective across various domains, including short-form academic reflections. Figure 3 presents a visual summary of the sentiment analysis approach using the VADER Model.

The VADER model uses a predefined sentiment lexicon in which each word w_i is assigned a valence score s(w_i), ranging from −4 (most negative) to +4 (most positive). For each student response D, the raw sentiment score was calculated as the following equation:

where:

1. s(w_i) represents the sentiment score of token w_i,

2. and n is the total number of tokens in the sentence.

To enhance contextual accuracy, VADER incorporates several heuristic rules that adjust raw scores:

1. Negation Handling: If a negation term (e.g. not, is not) appears within a three-word window before a sentiment-bearing word, the score is inverted:

1. Degree Modifiers: Words such as ‘very’, ‘extremely’, or ‘slightly’ adjust the intensity of the sentiment:

where α > 1 for intensifiers and 0 < α < 1 for dampeners.

Emphasis Handling: Use of capitalization or punctuation (e.g. “!!!”) further modifies intensity:

where δ ∈ [0.2, 0.3], depending on the emphasis detected.

After all adjustments, the following scores were computed for each response:

P = ∑ s′(w_i), for all w_i ∈ positive terms, N = ∑ |s′(w_i)|, for all w_i ∈ negative terms, U = count of neutral words.

The total sentiment magnitude was then determined as:

Normalized scores were computed as the following equation:

To derive an overall sentiment score, the following normalized compound score was calculated using the hyperbolic tangent function:

where α = 15 is a default scaling parameter.

Based on the compound score, sentiment was categorized according to the following empirically validated thresholds:

This classification allowed for an aggregated understanding of the emotional orientation expressed in student reflections on their interaction with GitHub Copilot.

To answer Research Questions One (RQ1) and Two (RQ2), content analysis was conducted to identify common themes in the students' responses, emphasizing both the technical and ethical challenges. RQ3 was explored using sentiment analysis, to determine the polarity of the students' responses which was then classified as either positive, neutral, or negative.

Our analysis of students' responses revealed, four emerging themes regarding technical challenges students faced when using GitHub Copilot to write code. Table 1 below outlines these themes along with selected responses of the technical challenges faced by the students. Out of the thirty-seven students surveyed, twenty-two percent (59%) responded to RQ1. Among the fifty-nine percent (59%) who provided feedback, twenty-seven (27%) indicated that they found using GitHub Copilot for coding to be either too complex or that it generated advanced code in response to their prompts. Additionally, thirty-two percent (32%) of the students reported challenges with GitHub Copilot's ability to interpret their prompts or noted that it provided incorrect solutions.

In analyzing students’ responses, three main themes emerged regarding the ethical challenges they faced while using GitHub Copilot for coding tasks. Table 2 below shows the themes along with selected responses of the ethical challenges encountered by the students. Thirty (30) out of the thirty-seven (37) students (81%) responded to RQ2. Among the eighty-one percent (81%) of students who provided feedback, thirty-nine percent (39%) indicated that they had concerns of plagiarism, nine-teen percent (19%) perceived that using GitHub Copilot limited their thinking. Additionally, twenty-four percent (24%) of the students perceived that they could become reliant on GitHub Copilot to write their code.

To address RQ3, sentiment analysis was conducted on student reflections using the VADER Sentiment Intensity Analyzer. All thirty-seven students surveyed submitted responses suitable for sentiment analysis. The VADER model computed compound sentiment scores for each response, which were then classified into positive, negative, or neutral categories using standard thresholds.

Figure 4 presents an overview of the VADER scoring and adjustment process. Based on the computed compound scores, 62% of the students expressed positive sentiments about using GitHub Copilot, often describing the tool as “helpful,” “time-saving,” and “improving coding confidence.” 11% of responses were classified as neutral, typically describing factual usage without emotional tone, such as “Copilot completed my code after writing a few lines.” 27% of the students expressed negative sentiments, often citing frustration, confusion, or unhelpful suggestions from the tool. These findings suggest that while the majority of students perceived GitHub Copilot positively, a notable minority experienced ambivalence or dissatisfaction, highlighting the need for additional training or guidance to optimize its use in educational settings.

The integration of artificial intelligence (AI), particularly Large Language Models (LLMs), has significantly impacted computer science education by transforming how coding is taught and learned. Tools like GitHub Copilot, powered by models such as OpenAI's Codex, offer real-time code generation, debugging assistance, and syntax explanations, especially useful in introductory programming courses. Our study investigates the integration of GitHub Copilot into a CS1 classroom at a university in the Caribbean. We report on a study in which undergraduate computing students evaluated their experiences using Copilot during programming exercises. Our findings contribute to the growing body of literature on human AI collaboration in educational and creative contexts (e.g. Barke, James, & Polikarpova, 2023), while offering culturally situated insights into the adoption and perception of AI tools in a Caribbean higher education setting.

First, we explored the technical challenges that the students experienced while using GitHub Copilot. Several students pointed out that the tool sometimes produced a code that was overly complicated or not suitable for the intended task, which caused confusion. This suggests that GitHub Copilot provided solutions that were more advanced than the programming level of the students. Similar findings were reported by Zhou and Li (2023). Some students reported that the suggestions from Copilot did not always work as expected or needed further debugging, interrupting their workflow (Armovic et al., 2024). Additionally, a few students faced difficulties in crafting effective prompts, noting that ambiguous or unclear instructions often resulted in irrelevant or unusable code snippets. This supports the discussion by Wermelinger (2023a, b) who emphasized the importance of the practice of prompt engineering when using Copilot for more accurate solutions.

Secondly, we explored the ethical challenges that students experienced using GitHub Copilot. The students raised concerns surrounding the ambiguity of referencing when utilizing AI-generated code. Students were unclear about how to properly acknowledge work created by Copilot (Gonzalez et al., 2022). Several Students expressed inner conflict about including AI-generated material in their submissions, while others feared that excessive reliance on the tool could hinder their ability to develop new skills. These findings suggest that educators are to engage students in dialog on the outputs of Copilot to promote a deeper understanding of coding practices, as advised by Denny et al. (2023).

Lastly, we examined students' sentiments toward GitHub Copilot. While the responses were varied, the majority shared positive sentiments. Several students reported an increase in their confidence and efficiency while using the tool, highlighting quicker task execution and decreased frustration when facing coding challenges. Some students felt neutral about the experience, acknowledging that while Copilot was beneficial, it did not profoundly impact their learning. Negative opinions primarily stemmed from confusion about how to effectively utilize the tool. This sentiment could be attributed to the students’ limited skills in using the tool and inexperience in programming. The authors believe that with more practice in both the use of the tool and in programming these negative sentiments would change. In addition, students also had concerns regarding reliance on Copilot and its ethical implications (Mo et al., 2025). These sentiments highlight the need for educators to foster students' critical evaluation abilities and to adjust instructional approaches in ways that leverage Copilot's advantages while addressing its drawbacks (Zhou and Li, 2023).

The role of the modern programmer is undergoing a profound transformation, largely driven by the rapid evolution of automated software development tools integrated into modern integrated development environments (IDEs). Tools powered by large language models (LLMs), such as GitHub Copilot and ChatGPT, are significantly reducing the need for programmers to spend extended periods writing and debugging code. As a result, the emphasis is shifting toward writing clear specifications, designing robust algorithms, and collaborating effectively with AI tools to co-create software (Finnie-Ansley, Denny, Becker, Luxton-Reilly, & Prather, 2022). This shift underscores the emerging paradigm of human-AI co-creativity in software engineering, where developers engage with AI agents in iterative and collaborative design processes.

In CS1 courses, the foundational introduction to programming, this shift presents both opportunities and challenges. Instructors have found it increasingly difficult to engage students using traditional exercises and introductory programming tasks. Many of these tasks, such as writing basic loops or conditionals, can now be solved with minimal human effort, even when prompted vaguely, through LLMs. As a result, students may perceive a diminished value in their learning efforts, mistakenly believing that AI tools are replacing rather than augmenting their cognitive work. This has led to a pedagogical challenge where educators must now reimagine the CS1 learning experience to focus on higher-order skills such as problem formulation, abstraction, and critical evaluation of AI-generated code.

The findings of the study are promising. In a subject widely recognized as cognitively demanding, students reported reduced anxiety, improved problem-solving confidence, and enhanced engagement. These outcomes suggest that AI tools like GitHub Copilot may play a role in combating “codephobia”, the fear or anxiety around learning programming and positively enhance students' learning experiences when used responsibly. Additionally, our findings initiate a broader philosophical dialog about the competencies required of future programmers. As AI models continue to advance in speed and accuracy, foundational courses such as CS1 must evolve to prioritize strategic thinking, ethical awareness, specification design, and critical collaboration with AI agents.

The implications of this study are both practical and theoretical. They suggest that curriculum designers and instructors must re-evaluate learning objectives to ensure that students are equipped with the cognitive and collaborative skills necessary for the AI-augmented future of programming. Future research should further explore how culturally and contextually responsive pedagogies can support equitable and effective AI integration across diverse educational settings.

One key limitation of this study is the relatively small sample size, which may affect the generalizability of the findings. Additionally, while the paper provides valuable insights into students' experiences and the challenges, they faced using GitHub Copilot, an LLM-powered code generation tool, the data is primarily based on subjective self-reported reflections. These perspectives, though critical to understanding user experience and ethical considerations, are not supported by quantitative measures that could link student perceptions to objective outcomes.

As a result, the study does not directly assess the impact of GitHub Copilot on student performance in formal assessments, such as examinations, or on measurable gains in productivity. Specifically, it remains unclear whether the use of AI-assisted coding tools enabled students to tackle more advanced problems or explore concepts typically beyond the scope of the course when traditional or solo programming methods are used.

The teaching of introductory programming or “Computing 1” (often referred to as CS1) has been significantly impacted by the emergence of large language models (LLMs) such as GitHub Copilot, ChatGPT, and others. These tools are rapidly reshaping how programming is learned and practiced, and their integration into the educational space is both inevitable and transformative. Research has already shown that LLM-powered code assistants can enhance productivity and reduce cognitive load for novice programmers by offering real-time suggestions, debugging support, and code completion (Schouwenaar, Snijders, Zhang, & Willemsen, 2024; Nizamudeen, Gatti, Bouali, & Ahmed, 2024).

In the Caribbean context, early adoption and critical examination of these tools are especially important, given the need to modernize curricula and align with global trends in computing education. The strategic incorporation of LLMs into CS1 pedagogy can support more inclusive, adaptive, and scalable learning environments, particularly where access to experienced mentors or teaching assistants may be limited.

As part of future work, we intend to conduct a quasi-experimental or randomized controlled trial to rigorously investigate the impact of GitHub Copilot on code comprehension, problem-solving, and student confidence. This study will address existing concerns among some students and instructors about over-reliance on AI tools and their potential to hinder deep learning. Key outcome measures will include learning gains, code quality (e.g. correctness, readability, and modularity), productivity (e.g. task completion time), and performance on traditional assessments such as exams and programming challenges.

We believe this study will contribute to a growing body of research that seeks to understand the evolving role of AI in computing education. By comparing outcomes between students who use LLM tools and those who do not, we aim to offer data-driven insights into how such technologies influence cognitive and affective dimensions of learning to code. Ultimately, this work will help educators in the Caribbean and beyond make informed decisions about the integration of LLMs in foundational computing courses, ensuring that students not only learn how to code but also how to think computationally and solve problems effectively.