Developing Students’ Ethical Reasoning and Cyber Responsibility through Generative AI- Driven Learning Tasks

Developing Students’ Ethical Reasoning and Cyber Responsibility through Generative AI- Driven Learning Tasks | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Developing Students’ Ethical Reasoning and Cyber Responsibility through Generative AI- Driven Learning Tasks Inuusah Mahama, Jane Odurowaa Edjah, Regine Kwaw, Munkaila Seibu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8855662/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The rapid integration of generative artificial intelligence (GenAI) into higher education has heightened ethical concerns related to academic integrity, responsible technology use, and cybersecurity. Despite growing interest in GenAI, empirical evidence on its role in shaping students’ ethical reasoning and cyber responsibility remains limited. Therefore, this study investigated the problem using a quantitative cross-sectional design, where data were collected from 423 undergraduate and postgraduate students enrolled in ICT-related courses. Three newly developed instruments: the Engagement with Generative AI–Driven Learning Tasks Scale, the Ethical Reasoning in Academic and Digital Contexts Scale, and the Cyber Responsibility in AI-Mediated Learning Scale developed and validated through exploratory factor analysis while descriptive statistics, Pearson correlations, and regression analyses were employed used. Results showed that engagement significantly predicted ethical reasoning, accounting for 17% of its variance, and strongly predicted cyber responsibility, explaining over 52% of the variance, while accountability emerged as the strongest ethical reasoning dimension associated with GenAI engagement. Furthermore, ethical reasoning predicted cyber responsibility. The findings suggest that well-designed GenAI-driven learning tasks can promote ethical reasoning and cyber responsibility. Implications are discussed for curriculum design, pedagogy, and policy development in AI-integrated higher education, particularly in the Global South. Generative artificial intelligence Ethical reasoning Cyber responsibility AI-mediated learning Higher education Figures Figure 1 Figure 2 Figure 3 Introduction The growing integration of generative artificial intelligence (GenAI) into academic research has created urgent ethical issues that necessitate the establishment of policies for the responsible use of these tools (Li et al., 2023; Zohny et al., 2023). According to Alduais et al. ( 2025 ), GenAI represents an evolution of traditional AI systems that is focused on creating original content, therefore, its potential has transformative implications for academia. The increased use of GenAI, however, has generated debates regarding how these tools will impact academic integrity, authorship and quality of scholarly publications (Lin, 2023; Zohny et al., 2023). As societies increasingly take advantage of these tools and systems, there has been a growing focus on the need for greater cybersecurity threats (e.g., Store and Forward Attacks, SQL Injection Attacks, Denial of Service Attacks, Cyber Bullying, and Phishing) among individuals, organisations and nations (Alhashimi et al., 2025 ; Ibrar et al., 2025 ). Even though there are many technological strategies in mitigating the threats of cybersecurity (Branham, 2024 ; Spyropoulos, 2025 ), it is believed that cybersecurity is not only a technological issue but also a human issue and therefore requires the same considerations as other issues relating to ethics and morality (Mahmood et al., 2024 ; Petrie-Wyman et al., 2021 ). Drawing from the ideas of Matei et al. ( 2025 ), ethical reasoning within cybersecurity is conceptualised as the ability to identify and analyse the ethical dilemmas associated with cyber actions and to evaluate the potential effects of your actions regarding the issues of privacy, security, legality and the overall impact on society. This conceptualisation is advanced through the lenses of cyber responsibility- personal accountability regarding the privacy, ownership and responsible use of GenAI and digital technology. Traditionally, education related to cybersecurity places premium on skills related to implementing cybersecurity programmes (e.g., coding for systems and web applications, defending networks, performing systems administration etc.) at the expense of ethical judgment and moral reasoning skills related to cybersecurity programme implementation (Gargiulo et al., 2024 ; Tsado & Kim, 2022 ; Ullah et al., 2025 ). With the current advancement in GenAI, teachers and professionals in cybersecurity education need to review their procedures of creating and imparting ethical education in the realm of cybersecurity. With this, GenAI enables learning situations of students that reflect realistic and contextual ethical procedures in cybersecurity (Đerić et al., 2025 ; Eacersall et al., 2025 ; Elkhodr & Gide, 2025 ). Again, students’ use of GenAI presents to them opportunities to simulate situations and incorporate various ethical dilemmas associated with misappropriation of data, social engineering and various other types of insider threats and cybercrime (Olohunfunmi & Khairuddin, 2024 ; Takgil, 2025 ). Numerous ethical dilemmas such as plagiarism, misinformation and questions of authenticity exist when GenAI tools are adopted in academic activities (Al-Hajaya, 2025 ; Olohunfunmi & Khairuddin, 2024 ). Besides, issues of clarity exist in determining apply AI contributions in academic activities and the ownership of information generated (Carobene et al., 2024 ; Vasylyshyna et al., 2024 ). Furthermore, there is a likelihood of advancing prevailing biases through AI-generated outputs and to create inaccurate outputs due to the use of incorrect training sets (Huang & Huang, 2025 ; York et al., 2024 ). Thus, people using GenAI should have stringent oversight over what and how they prompt to moderate these risks (Beltran et al., 2024 ; Gehrmann et al., 2025 ). Making a declaration on the use of AI in academic tasks is an important process for promoting honesty, integrity and academic transparency in academic space (Gulumbe et al., 2025 ; Güneş & Kaban, 2025 ). However, the lack of comprehensive guidelines on the ethical use and implications of GenAI in academic activities demand a comprehensive and robust user guidelines in applying GenAI tools in academic spaces (An et al., 2025 ; Symeou et al., 2025 ; Wang et al., 2024 ). Theoretical Framework Primarily, the study dwells on Lawrence Kohlberg’s moral development theory, James Rest’s ethical reasoning, and Zimmerman’s self-regulation theory. Kohlberg’s (1969, 1984) conceptualised moral and ethical reasoning through phases: pre-conventional, conventional, and post-conventional stages. At the pre-conventional stage, moral judgments become personal and are made purposely to avoid behavioural consequences (e.g., punishment). In the contexts of cybersecurity, every action of students is primarily guided by acting appropriately with digital information to avoid repercussions and promote positive ethical behaviours. At the conventional stage, actions of students are governed by institutional expectations, societal standards, and complying with established protocols, purposely to maintain harmony. Therefore, students may perceive ethical behaviour in cybersecurity as abiding by the institutional policies, legal frameworks, or professional codes of conduct. At the post-conventional stage, actions of students are fused with abstract thoughts beyond simple imagination. Societal common good for their actions becomes the focal point, where they interpret moral and ethical situations toward general human growth and development. In relating to cybersecurity education, students consider societal consequences when taking cyber actions. Students may make judgement on general gains against societal setbacks. They may engage in responsible technology use even when situations are not clear. Taken together, this model is relevant in cybersecurity education as students are expected to only comply to rules but become principled in their reasoning when challenged by difficult ethical situations such as data privacy, surveillance, and responsible AI use. GenAI-driven learning tasks support this progression by presenting students with novel cyber situations that challenge simplistic, rule-based responses. Supplementing Kohlberg’s work, Rest’s (1986) Four-Component Model of morality such as moral sensitivity, moral judgment, moral motivation, and moral character provides a nuanced lens for appreciating ethical actions among people. Specific to this study, GenAI-produced cybersecurity scenarios improve moral understanding by creating clear ethical conflicts, while at the same time insightful tasks support moral judgment and motivation. This viewpoint is predominantly striking in the contexts of cybersecurity, where some decisions that may be unethical affect individuals, communities, and vulnerable populations. Taken together, this theory offers a solid basis for probing how GenAI-driven learning tasks can inure to ethical reasoning and cyber responsibility among students. In the case of Zimmerman (2000), self-regulated learning is conceptualised as a recurring process of learning relating to planning, performance monitoring, and self-reflection. Relating to cybersecurity education, students are required to autonomously plan their responses, monitor their reasoning, and evaluate the ethical consequences of their own actions. These processes are supported by GenAI-driven learning situations where students take responsibility in defending their decisions appropriately, adapting to new approaches, and reflect on outcomes in the realm of cybersecurity. Zimmerman’s view is supported with a focus on human motivation and metacognitive regulation in the process of learning related ethically complex cyber situations (Purwaningsih, 2024 ; Tsai et al., 2024 ). Taken together, self-regulated learning among students explains that GenAI-enhanced learning environments can promote sustained ethical and responsible cyber behaviours. The Ghanaian Context In light of the challenges presented by using GenAI, stakeholders across academia and other sectors have begun developing guidelines and policies for the ethical deployment of GenAI (Alduais et al., 2025 ; Zlotnikova et al., 2025 ). For instance, in Ghana, while there is an increasing interest among educators in the potential use of GenAI, limited research exists that addresses the role of GenAI in the development of ethical reasoning and cyber responsibility among learners (Bannor et al., 2025 ; Suglo et al., 2024 ). Existing research on AI in education primarily focuses on academic performance, engagement and efficiency of students, but not on the learning outcomes related to ethical education of students (Asamoah & Amarteifio, 2025 ; Baidoo-Anu et al., 2024 ). In addition, there is a growing number of concerns surrounding GenAI in regards to issues related to bias, misinformation, over-dependence upon automated systems and the potential normalization of unethical behaviour attributed to the use of GenAI (Salyer, 2024 ; Shittu et al., 2025 ). Therefore, it is essential that research examines how GenAI will affect not only teaching methods used for ethics, but also assists learners in developing ethical reasoning and a heightened sense of cyber responsibility so that curriculum development, instructional practices and educational policy are aligned with the integration of artificial intelligence within cybersecurity education in the Global South. Research Questions To what extent do generative AI-driven learning tasks influence students’ ethical reasoning in digital contexts? How does students’ engagement with generative AI-driven learning tasks affect students’ levels of cyber responsibility? What dimensions of ethical reasoning are most strongly associated with participation in generative AI-supported learning activities? What is the influence of students’ ethical reasoning and their cyber responsibility when learning tasks are mediated by generative AI tools? Methods and Materials Research Design and Rationale The study used a quantitative, analytic cross-sectional design. The choice of this methodological procedure was informed by the fact of establishing effects and relationships among the study’s constructs (e.g., engagement, ethical reasoning, cyber responsibility) which occur naturally in any learning environment. In line with best practices, the current study stresses associational claims and infers the study results cautiously in terms of causality (Paing, 2024 ). Practically, the included undergraduate and postgraduate students who pursued ICT courses that employed generative AI tools to support the learning procedures. Specifically, we designed academic activities that relate to generative AI-driven learning tasks in which students applied ChatGPT 4.o to produce initial ideas (e.g., data privacy, academic dishonesty, misinformation, and responsible AI use, misuse of generative AI for assignments), refine their outputs through iterative prompts (e.g., students’ perspectives of student, teacher, institution, society, data protection, algorithmic bias), judge the worth of their outputs (e.g., ascertaining whether AI-generated recommendations for handling online ethical dilemmas were credible, ethical, and socially responsible, respect for privacy, avoidance of misinformation), and provide justifications for these processes in using the AI tool (e.g., maintaining reflective logs documenting how AI outputs informed their ethical reasoning, explicitly documenting instances where AI-generated solutions were rejected because they conflicted with ethical principles such as honesty, accountability, or respect for others’ digital rights). In making engagement reflective in the students’ activities, we explicitly incorporated accountability prompts (e.g., “What did you accept, revise, or discard and why?”) that allowed students to respond reflectively to components that were related to academic integrity and responsible digital conduct as they use AI tools. Participants and Sampling The respondents for this study were 423 (undergraduate = 271; postgraduate = 153) drawn from diverse year groups of students pursuing academic courses related to ICT. The recruitment process was random and voluntary, where every student in the specified category had the chance to be part and had the freedom to participate or not to participate. The number of respondents recruited for the study was deemed appropriate and adequate for estimating statistical relationships and effects among the various constructs studied. The selection of cases in this study emphasised ecological validity in relation to the study findings because the study constructs were examined in a real instructional setting by recognising that generalizability of the study findings is contextualised to the participating institution. In terms of respondents’ inclusion eligibility, one must be enrolled as a regular student as of the time of data collection, and one must be taking part in an ICT course that integrates AI in learning tasks. With respect to respondents’ exclusion eligibility, one must be unwilling to participate, incomplete consent to participate, must be pursuing courses that do not integrate AI tasks, and must be absent from class as of the time of data collection. Measures and Operationalisation of Constructs Students’ Engagement in AI task The instrument used in measuring students’ engagement in AI task was a continuous composite from 20-items distributed among three dimensions: behavioural engagement (7-items, α = .75), cognitive engagement (7-items, α = .73), and reflective engagement (6-items, α = .76). All the items on the instrument reflected the extent to which students took part in AI-supported learning activities actively. The instrument was scored on a Likert-type (e.g., SD = 1 through to SA = 5) and the responses were recorded in that sense. The composite engagement score was calculated as the average of all 20 items. In determining whether the developed scale meets the requirement for the current study, an exploratory factor analysis (EFA) was performed on the 20 items through principal axis factoring (PAF) with Promax rotation (PR). The EFA results showed adequacy of the sampling with KMO= .93, and a significant Bartlett’s Test of Sphericity was significant [( χ² (190) = 4861.42, p < .001)]. All the three factors extracted produced eigenvalues greater than 1. Again, all the 20 items loaded appropriately and strongly (see Appendix A; Fig. 1 ) while the total variance explained was 59.9%. Ethical Reasoning in AI Tasks The instrument used in assessing ethical reasoning in AI tasks was a conceptualised multidimensional tool that reflected how students used ethical judgment in digital learning situations where AI is embedded. In all, the instrument had 20-items, placed under four dimensions: moral judgment (5-items, α = .78), accountability (5-items, α = .74), fairness (5-items, α = .71), and autonomy (5-items, α = .79). The scale was developed on a five-point Likert-type scale (Strongly Disagree = 1 to Strongly Agree = 5). The composite of this instrument was calculated from all the 20-items as an average of all the four dimensions. Furthermore, an EFA was performed on the 20 items under scale and it produced four-factor solution indicating Moral judgment, accountability, fairness, and autonomy. The KMO value was .91, while Bartlett’s Test was significant [ χ² ( 190) = 5124.36, p < .001)]. Again, the items under the scale loaded strongly (see Appendix A; Fig. 2 ), while the total variance explained was 61.3%. Cyber Responsibility in AI Tasks In this study, cyber responsibility was conceptualised as the way students conduct themselves responsibly and their decision-making procedures in technology-mediated learning situations where generative AI tools are used. The instrument used to assess the cyber responsibility was a 20-item inventory, distributed among three dimensions: responsible data use (7-items, α = .77), academic integrity (7-items, α = .75), and online conduct (6-items, α = .79). The scale was developed on a five-point Likert-type scale (Strongly Disagree = 1 to Strongly Agree = 5). The composite of this instrument was calculated from all the 20-items as an average of all the four dimensions. Aside from these, an EFA was performed on the 20 items and it showed a three-factor structure. The results of this process produced a KMO of .92, and Bartlett’s Test of [( χ² ( 190) = 4978.09, p < .001)]. In all, the items loaded appropriately and strongly (see Appendix A; Fig. 3 ) while the variance explained was 57.1%. Data Analysis Primarily, the data gathered were screened and cleaned to ensure accuracy, quality, distributional properties, and appropriateness before the analyses. to assess data. Again, EFAs were conducted on all the constructs because their items were new and it was appropriate to establish latent structure and construct validity for all using Partial Least Squares Structural Equation Modelling [PLS-SEM] (Zia et al., 2025 ). Lastly, regression analyses were performed in relation to each question at a statistical significance threshold of .05. Results The study explored the degree to which generative AI-driven learning tasks influence ethical reasoning in academic and digital contexts of students. The data for this study were quantitative, where descriptive (e.g., means and standard deviations) and inferential (e.g., multiple linear regression) statistical analyses were performed using SPSS v23. The analyses and presentation of the results were based on the research question proposed for the study. Research Question One To what extent do generative AI-driven learning tasks influence students’ ethical reasoning in academic and digital contexts? The descriptive results showed a moderate to high level of exposure to generative AI-driven learning tasks among students (M = 3.61, SD = 0.72) while ethical reasoning in academic and digital contexts by students showed a high level (M = 3.74, SD = 0.68). The descriptive results were further expatiated with the regression results, where statistically significant results were established for the regression model [ F (1, 421) = 97.84, p < .001]. The results showed that generative AI-driven learning tasks significantly predicted ethical reasoning (B = 0.41, SE = 0.04, β = .43, p < .001) with 17% of the variance in ethical reasoning explained by generative AI tasks. By implication, an increased engagement with AI-driven learning tasks among students associate with higher levels of ethical reasoning in digital learning contexts. These can be found in Table 1 . Table 1 Multiple Linear Regression Predicting Ethical Reasoning from Generative AI-Driven Learning Tasks Predictor B SE β P Generative AI-Driven Learning Tasks .41 0.04 .43 < .001 Note : N = 423; R² = .17; B = unstandardized coefficient; β = standardized coefficient Research Question Two To what extent does engagement with generative AI-driven learning tasks affect students’ levels of cyber responsibility? Table 2 Descriptive Statistics for Engagement and Cyber Responsibility Outcomes Variable M SD Engagement AI Tasks 3.42 0.63 Cyber Responsibility 3.23 0.44 Responsible Data Use 3.15 0.62 Academic Integrity 3.24 0.68 Online Conduct 3.30 0.59 The descriptive results in Table 2 showed a moderate engagement with AI-driven learning tasks by students (M = 3.42, SD = 0.63) while cyber responsibility a high level (M = 3.23, SD = 0.44). Table 3 Pearson Correlations Between Engagement and Cyber Responsibility Outcomes Outcome r p Cyber Responsibility .713 .001 Responsible Data Use .496 .001 Academic Integrity .575 .001 Online Conduct .425 .001 In Table 3 , the results showed that students’ engagement with AI-driven learning tasks related positively with overall cyber responsibility (r = .713, p < .001). At the dimension level, students’ engagement with AI-driven learning tasks related positively with responsible data use (r = .496, p < .001), academic integrity (r = .575, p < .001), and online conduct (r = .425, p < .001). Jointly, these bivariate relationships showed that when students’ engagement with AI-driven learning tasks increases, their cyber responsibility equally increases. Table 4 OLS Regression Predicting Cyber Responsibility Composite from Engagement With AI-Driven Tasks Composite Predictor B SE β t p 95% CI [LL, UL] Engagement AI Tasks .495 .024 .705 20.81 .001 [.448, .542] Gender Male − .076 .030 − .085 -2.51 .013 [-.135, − .016] Prior AI Training .046 .031 .050 1.48 .140 [-.015, .107] Furthering the analyses to ascertain the exclusive effect of students’ engagement with AI-driven learning tasks on cyber responsibility while taking into consideration covariates, we performed an Ordinary Least Square (OLS) regression model by fitting the composite score of cyber responsibility as a criterion variable and using engagement, sex of students, year of study, and prior AI training as explanatory variables in Table 4 . From these analyses, students’ engagement with AI-driven learning tasks significantly predicted their cyber responsibility [B = .495, β = .705, SE = .024, t (418) = 20.81, p < .001, 95% CI [0.448, 0.542]. With this significant model, 52.5% of the variance in cyber responsibility was explained by students’ engagement with AI-driven learning tasks. However, sex (coded 1 = male) indicated a small significant but an inverse relationship with cyber responsibility (B = − .076, p = .013, 95% CI [-.135, − .016]), while prior AI training was not significant statistically significant (B=.046, p = .140, 95% CI [-.015, .107]). This pattern suggests that engagement with AI-driven learning tasks is the dominant predictor of responsible cyber-related attitudes and behaviours in this dataset, above and beyond basic demographic and variations in exposure. Research Question Three What dimensions of ethical reasoning are most strongly related to participation in generative AI-supported learning activities? In responding to the question, we performed Pearson Product–Moment (PPM) correlation and multiple linear regression at .05 significant level. Table 5 presents the results. Table 5 Correlations and Regression Results for Ethical Reasoning Dimensions Predicting Generative AI Engagement Predictor M SD r β P Moral Judgment 3.84 .56 .48 .31 .001 Accountability 3.92 .52 .55 .38 .001 Fairness 3.76 .60 .42 .21 .002 Autonomy 3.58 .63 .29 .12 .041 In Table 5 , the results showed a statistically significant positive relationships between students’ engagement in generative AI-supported learning activities and the dimensions of ethical reasoning. For instance, accountability had the highest relationship (r = .55, p < .001), followed by moral judgment (r = .48, p < .001), fairness (r = .42, p = .002), and autonomy (r = .29, p = .041). Furthermore, the regression model was significant [ F (4, 418) = 72.64, p < .001)] and the results showed that the dimensions of ethical reasoning together accounted for 41% of the variance in students’ engagement with generative AI-supported learning activities. Specifically, the accountability dimension was the highest predictor (β = .38, p < .001), followed by moral judgment dimension (β = .31, p < .001), then the fairness dimension (β = .21, p = .002), and the autonomy dimension (β = .12, p = .041). Research Question Four What is the relationship between students’ ethical reasoning and their cyber responsibility when learning tasks are mediated by generative AI tools? In answering this question, the data were tested using Pearson Product–Moment (PPM) correlation and Simple Linear Regression (SLR). Table 6 presents the results. Table 6 Correlation and Simple Linear Regression Predicting Cyber Responsibility from Ethical Reasoning Predictor B SE B β t p R R 2 Adj. R 2 F p Constant 1.12 0.18 — 6.22 .001 Ethical Reasoning .55 0.07 .55 7.86 .001 .541 .293 .289 61.78 .001 Pearson correlation analysis revealed a statistically significant, positive relationship between ethical reasoning and cyber responsibility, r = 0.54, p < .001. This indicates that students who demonstrated higher levels of ethical reasoning also tended to report higher levels of cyber responsibility when engaging with generative AI–supported learning tasks. The simple linear regression [ F (1, 148) = 61.78, p < .001] further showed that ethical reasoning significantly predicted cyber responsibility, β = 0.55, p < .001, explaining approximately 29.3% of the variance in cyber responsibility (R² = 0.29). This suggests that ethical reasoning is a substantive cognitive–moral factor underpinning responsible digital behaviour in AI-mediated learning environments. Discussion This study examined how engagement with generative artificial intelligence (GenAI)–driven learning tasks shapes students’ ethical reasoning and cyber responsibility in academic and digital contexts. Taking into consideration theoretical perspectives such as moral development and self-regulated, the study findings offer strong practical evidence that GenAI can work as a powerful instructive catalyst for ethical thinking and accountable digital conduct. Specifically, this revelation aligns with the post-conventional stage of moral reasoning proposed by Kohlberg, as students may judge their actions in relation to values and standards establish by the broader institutional community. Significantly, the findings illuminate discourse on GenAI beyond performance and efficiency outcomes by dwelling on ethical development and cyber responsibility (Bannor et al., 2025 ; Suglo et al., 2024 ). Similarly, the study showed that students’ ethical reasoning abilities were influenced by their engagement with GenAI-driven learning tasks in the era of digital technologies. This finding corroborates the evolving arguments that GenAI tools are not just tools for productivity but a moral learning environment possibly to evoke reflective judgment, ethical awareness, and principled decision-making among students’ users (Alduais et al., 2025 ; Đerić et al., 2025 ). Importantly, GenAI-driven activities commonly present vague, poor-structured problems that require students to extend their reasoning abilities and engage in abstract ethical judgement (Zohny et al., 2023; Lin, 2023). Again, the study revealed a significant direct positive association between students’ engagement with GenAI-driven learning tasks and their cyber responsibility. Surprisingly, this is noteworthy, as it challenges predominant traditions that the higher one uses AI, the higher they are tempted to become dishonest in their use through ethical erosion (Al-Hajaya, 2025 ; York et al., 2024 ). By implication, the findings suggest that the ways students engage with GenAI is important than whether they engage at all in the learning setting, which points to self-regulatory abilities of students. For instance, tasks in GenAI demand planning on prompts, outputs, evaluation, and making a decision. Such procedures reflect the foresight, effort, and self-reflection phases of self-regulation (Zimmerman, 2000). Empirically, students with higher self-regulatory abilities show higher ethical awareness and cyber responsibility because they actively evaluate consequences and adjust behaviour accordingly (Purwaningsih, 2024 ; Tsai et al., 2024 ). Furthermore, the study found that all dimensions of students’ ethical reasoning in AI use significantly related positively with their participation in GenAI-supported learning activities, where accountability became the highest predictor. This revelation possessed theoretically and practically connotation. For example, accountability in itself brings about ownership to decisions made or taken by students, and are always willing to take responsibility for such decisions (Carobene et al., 2024 ; Vasylyshyna et al., 2024 ). Therefore, when students record high scores in accountability, they may use GenAI meaningfully and responsibly because every action in that sense becomes a choice. This aligns with previous studies indicating that a clear reflection on responsibility in the use of AI reduces misconduct in the use of AI (Beltran et al., 2024 ; Gulumbe et al., 2025 ). Lastly, the revealed that ethical reasoning predicted cyber responsibility strongly and significantly. This outcome affirms the assertion that ethical reasoning in AI use serves as a basic cognitive–moral mechanism supporting responsible digital behaviours among students. The finding aligns with current cybersecurity ethics scholarship, which contends that methodological precautions are insufficient in guiding appropriate cyber behaviours among students without a concerted ethical judgment protocol towards responsible user behaviour (Branham, 2024 ; Mahmood et al., 2024 ). Literary, students who can reason ethically are better positioned to navigate dilemmas involving data privacy, misinformation, social engineering, and AI misuse (Olohunfunmi & Khairuddin, 2024 ; Takgil, 2025 ). Conclusions Based on the study findings, it is concluded that GenAI can meaningfully contribute to students’ ethical development educational curricula because GenAI-supported learning environments can serve as ethical learning spaces for students in Ghana. Again, GenAI-driven learning tasks primarily influence how students’ reason ethically and behave responsibly in digital space and not about reshaping they learn. As GenAI is purposely integrated in global education systems, such intuitions may provide a compelling evidence base for reimagining AI as moral learning opportunity and not as a moral risk. Recommendations for Policy and Practice Higher education institutions should develop clear, context-sensitive policies governing the ethical use of generative AI in teaching, learning, and assessment. These policies should move beyond prohibition and instead emphasize responsible use, transparency, accountability, and ethical reasoning as core learning outcomes. Managers of HEIs in collaboration with the Ministry of Education and Ghana Education Service, should explicitly embed ethical reasoning, cyber responsibility, and AI literacy into higher education curriculum frameworks. This is particularly important in the Global South, where policy gaps may exacerbate misuse or inequitable adoption of AI technologies. Faculty should design GenAI-supported tasks that explicitly require students to justify AI use, evaluate ethical implications, and reflect on decision-making processes. Such tasks strengthen accountability, moral judgment, and self-regulation. HEIs should provide ongoing professional development for lecturers and instructional designers focused on ethical AI pedagogy, including how to facilitate discussions on bias, privacy, fairness, and responsible AI use. Contextual Implication The integration GenAI in education can help promote ethical capacity building and reduces the apprehension of its threat to academic integrity. However, the findings of this study are contextually salient in Ghana. Although Scholars in Ghana have begun to embraces GenAI in their activities, ethical frameworks and institutional guidelines remain blurry (Bannor et al., 2025 ; Baidoo-Anu et al., 2024 ; Suglo et al., 2024 ). Suggestions for Further Research In furthering this area, experimental studies comparing different types of GenAI-driven learning tasks (e.g., reflective vs. non-reflective designs) would provide stronger causal evidence regarding which pedagogical approaches most effectively promote ethical outcomes. Again, comparative studies across countries and regions within the African Continent would deepen understanding of how cultural, institutional, and policy contexts mediate the relationship between GenAI engagement, ethical reasoning, and cyber responsibility. Declarations Ethics Approval and Consent to Participate This study was conducted in accordance with established ethical standards for research involving human participants. Ethical approval for the study was obtained from the Institutional Review Board (IRB) of the University of Education, Winneba prior to data collection (UEW-IRB-01-25). The research procedures complied with institutional guidelines and the principles outlined in the Declaration of Helsinki. Participation in the study was voluntary. Before completing the survey, participants were provided with a detailed information sheet explaining the purpose of the study, the nature of their involvement, potential risks and benefits, confidentiality assurances, and their right to withdraw at any time without penalty. Informed consent was obtained electronically from all participants before they were allowed to proceed with the questionnaire. No personally identifiable information was collected. Responses were anonymized at the point of data collection, and data were stored securely on password-protected devices accessible only to the research team. The study posed minimal risk to participants, as it involved self-reported perceptions related to engagement with generative AI-driven learning tasks, ethical reasoning, and cyber responsibility in academic contexts. Acknowledgments We appreciate all collaborators for their effort in this study. Above all, we thank our participants for availing themselves for this survey. Data Availability Statement The data can be given out when a formal and an ethical request is made to the authors. Declaration of interest statement We have no competing interests to declare. Consent for Publication Not applicable. Declaration on the use of Generative AI The researchers employed the services of ChatGPT and Quill Bot in checking grammar and flow of ideas in the write-up. Funding This research received no external funding. The study was conducted as part of the authors’ academic and institutional research activities. Authors’ Contributions IM conceptualized the study, developed the theoretical and analytical framework, designed the methodology, conducted the statistical analyses, and drafted the original manuscript. He also coordinated the overall research process. JOE contributed to the refinement of the research design, validated the analytical procedures, and critically reviewed the manuscript for intellectual rigour. RK supported data collection and management, contributed to literature synthesis, and participated in manuscript revision. MS contributed to the theoretical framing of the study, provided high-level academic supervision, and critically revised the manuscript. SOD assisted with statistical interpretation, supported data presentation and visualization, and reviewed the manuscript. PE provided strategic oversight, contributed to the intellectual direction of the study, and critically revised the manuscript for important scholarly content. All authors read and approved the final manuscript. References Alduais A, Qadhi S, Chaaban Y, Khraisheh M. Utilizing Generative AI responsibly and ethically for research purposes in higher education: A policy analysis. 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Cybersecurity governance and normative frameworks: Non-Western countries and international organizations perspectives. Società Italiana Per L’organizzazione Internazionale, 1–320. Gehrmann S, Huang C, Teng X, Yurovski S, Bhorkar A, Thomas N, Rabinowitz D. (2025). Understanding and mitigating risks of Generative AI in financial services. In Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency (pp. 2570–2586). Gulumbe BH, Audu SM, Hashim AM. Balancing AI and academic integrity: What are the positions of academic publishers and universities? AI Soc. 2025;40(3):1775–84. https://doi.org/10.1007/s00146-024-01946-8 . Güneş A, Kaban AL. A Delphi study on ethical challenges and ensuring academic integrity regarding AI research in higher education. High Educ Q. 2025;79(4):e70057. https://doi.org/10.1111/hequ.70057 . Huang LTL, Huang TR. Generative bias: Widespread, unexpected, and uninterpretable biases in generative models and their implications. AI Soc. 2025;1–13. https://doi.org/10.1007/s00146-025-02533-1 . Ibrar W, Mahmood D, Al-Shamayleh AS, Ahmed G, Alharthi SZ, Akhunzada A. Generative AI: a double-edged sword in the cyber threat landscape. Artif Intell Rev. 2025;58(9):285. https://doi.org/10.1007/s10462-025-11285-9 . Mahmood S, Chadhar M, Firmin S. Addressing cybersecurity challenges in times of crisis: Extending the sociotechnical systems perspective. Appl Sci. 2024;14(24):11610. Matei SA, Jackson D, Bertino E. Ethical reasoning in artificial intelligence: A cybersecurity perspective. Inform Soc. 2025;41(2):110–22. https://doi.org/10.1080/01972243.2024.2429060 . Olohunfunmi IA, Khairuddin AZ. (2024). Exploring ethical dilemmas of AI generative tools among higher education students: A systematic review. In International conference on innovation & entrepreneurship in computing, engineering & science education (InvENT 2024) (pp. 255–275). Atlantis Press. 10.2991/978-94-6463-589-8_24 Paing L. (2024). Causal language and practice recommendations in observational clinical psychology research: a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Psychology at Massey University, Wellington, New Zealand (Doctoral dissertation, Massey University). Petrie-Wyman J, Rodi A, McConnell R. Why should I behave? Addressing unethical cyber behaviour through education. Developments Bus Simul Experiential Learn. 2021;48:22–38. Purwaningsih E. The role of metacognition in character education: a case study on students’ moral and ethical formation strategies. Society. 2024;12(1):1–13. https://doi.org/10.33019/society.v12i1.617 . Salyer CP. (2024). Artificial intelligence through young eyes: A study of students’ perspectives and experiences with artificial intelligence in education (Doctoral dissertation, Lincoln Memorial University). Shittu OI, Busari DA, Olonade OY. Knowledge, attitude, and perception of learners towards the use of ChatGPT in the University of Ibadan, Nigeria. Int J Sociol Educ. 2025;14(2):146–68. Spyropoulos F. Technoethics and hackers in a ‘hybrid’ world: thoughts in the light of digital criminology. J Cyber Policy. 2025;10(1):34–50. https://doi.org/10.1080/23738871.2025.2517656 . Suglo EK, Mejira A, Bayor N, Nyemewero I, Godfred AA. Integrating Generative AI in contemporary research writing: Exploring postgraduates’ knowledge and willingness to use GenAI in the Upper East Region of Ghana. Am J Technol. 2024;3(1):33–51. Symeou L, Louca L, Kavadella A, Mackay J, Danidou Y, Raffay V. Development of evidence-based guidelines for the integration of Generative AI in university education through a multidisciplinary, consensus‐based approach. Eur J Dent Educ. 2025;29(2):285–303. https://doi.org/10.1111/eje.13069 . Takgil B. Examining the ethical risks of Generative AI in cybersecurity: An experimental study on ethical, gray area and unethical usage scenarios. Siber Güvenlik ve Dijital Ekonomi. 2025;1(1):1–9. Tsado L, Kim JS. Assessing the practical cybersecurity skills gained through criminal justice academic programs to benefit security operations centres (SOCs). J Cybersecur Educ Res Pract. 2022;1(2):1–21. https://digitalcommons.kennesaw.edu/jcerp/vol2022/iss1/2 . Tsai CW, Lee LY, Cheng YP, Lin CH, Hung ML, Lin JW. Integrating online meta-cognitive learning strategy and team regulation to develop students’ programming skills, academic motivation, and refusal self-efficacy of Internet use in a cloud classroom. Univ Access Inf Soc. 2024;23(1):395–410. https://doi.org/10.1007/s10209-022-00958-9 . Ullah F, Ye X, Fatima U, Akhtar Z, Wu Y, Ahmad H. (2025). What skills do cyber security professionals need? arXiv preprint arXiv:2502.13658 . Vasylyshyna N, Skyrda T, Lazorenko N, Kravets I. (2024). Legal perspective on Artificial Intelligence and academic integrity within university education process participants research activity: New possibilities along with new limitations. Scientific Journals of the International Academy of Applied Sciences in Lomza , 96 (4), 59–82. https://orcid.org/0000-0002-0003-9998. Wang H, Dang A, Wu Z, Mac S. Generative AI in higher education: Seeing ChatGPT through universities' policies, resources, and guidelines. Computers Education: Artif Intell. 2024;7:100326. https://doi.org/10.1016/j.caeai.2024.100326 . York EJ, Brumberger E, Harris LVA. (2024). Prompting Bias: Assessing representation and accuracy in AI-generated images. In Proceedings of the 42nd ACM International Conference on Design of Communication (pp. 106–115). Zia N, Doss JG, Danaee M, John J, Panezai J. Psychometric analysis of a KAP questionnaire on green dentistry using PLS-SEM and EFA: a pilot study. BMC Oral Health. 2025;25(1):1353. Zlotnikova I, Hlomani H, Mokgetse T, Bagai K. Establishing ethical standards for GenAI in university education: A roadmap for academic integrity and fairness. J Inform Communication Ethics Soc. 2025;23(2):188–216. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8855662","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":594649745,"identity":"818c5f01-0186-4529-b15a-b03d2964e913","order_by":0,"name":"Inuusah 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Education","correspondingAuthor":false,"prefix":"","firstName":"Peter","middleName":"","lastName":"Eshun","suffix":""}],"badges":[],"createdAt":"2026-02-11 21:23:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8855662/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8855662/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103506950,"identity":"5ab479ee-d76e-4a43-aa60-7a65582f448c","added_by":"auto","created_at":"2026-02-26 13:40:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":143143,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual Model on Students’ Engagement in AI task\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8855662/v1/382c96d18a2ebae6022745ba.png"},{"id":103344104,"identity":"00734a3a-4f2e-462a-9af5-035eff24a469","added_by":"auto","created_at":"2026-02-24 16:01:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":139582,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual Model on Ethical Reasoning in AI Tasks\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8855662/v1/8714e940d1251e8cf1284efa.png"},{"id":103506066,"identity":"5f5a51af-a1c2-4ac6-b473-929248d45dbc","added_by":"auto","created_at":"2026-02-26 13:33:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":141381,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual Model on Cyber Responsibility in AI Tasks\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8855662/v1/32e9ecdcd0a8770ec8182062.png"},{"id":103509903,"identity":"0c582a89-6961-4ac9-8403-7617e70ded12","added_by":"auto","created_at":"2026-02-26 14:02:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1283463,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8855662/v1/22859a5b-a1ed-44b5-a44b-820c74ea93e1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Developing Students’ Ethical Reasoning and Cyber Responsibility through Generative AI- Driven Learning Tasks","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe growing integration of generative artificial intelligence (GenAI) into academic research has created urgent ethical issues that necessitate the establishment of policies for the responsible use of these tools (Li et al., 2023; Zohny et al., 2023). According to Alduais et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), GenAI represents an evolution of traditional AI systems that is focused on creating original content, therefore, its potential has transformative implications for academia. The increased use of GenAI, however, has generated debates regarding how these tools will impact academic integrity, authorship and quality of scholarly publications (Lin, 2023; Zohny et al., 2023). As societies increasingly take advantage of these tools and systems, there has been a growing focus on the need for greater cybersecurity threats (e.g., Store and Forward Attacks, SQL Injection Attacks, Denial of Service Attacks, Cyber Bullying, and Phishing) among individuals, organisations and nations (Alhashimi et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Ibrar et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Even though there are many technological strategies in mitigating the threats of cybersecurity (Branham, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Spyropoulos, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), it is believed that cybersecurity is not only a technological issue but also a human issue and therefore requires the same considerations as other issues relating to ethics and morality (Mahmood et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Petrie-Wyman et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDrawing from the ideas of Matei et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), ethical reasoning within cybersecurity is conceptualised as the ability to identify and analyse the ethical dilemmas associated with cyber actions and to evaluate the potential effects of your actions regarding the issues of privacy, security, legality and the overall impact on society. This conceptualisation is advanced through the lenses of cyber responsibility- personal accountability regarding the privacy, ownership and responsible use of GenAI and digital technology. Traditionally, education related to cybersecurity places premium on skills related to implementing cybersecurity programmes (e.g., coding for systems and web applications, defending networks, performing systems administration etc.) at the expense of ethical judgment and moral reasoning skills related to cybersecurity programme implementation (Gargiulo et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Tsado \u0026amp; Kim, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ullah et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). With the current advancement in GenAI, teachers and professionals in cybersecurity education need to review their procedures of creating and imparting ethical education in the realm of cybersecurity. With this, GenAI enables learning situations of students that reflect realistic and contextual ethical procedures in cybersecurity (Đerić et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Eacersall et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Elkhodr \u0026amp; Gide, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Again, students\u0026rsquo; use of GenAI presents to them opportunities to simulate situations and incorporate various ethical dilemmas associated with misappropriation of data, social engineering and various other types of insider threats and cybercrime (Olohunfunmi \u0026amp; Khairuddin, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Takgil, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNumerous ethical dilemmas such as plagiarism, misinformation and questions of authenticity exist when GenAI tools are adopted in academic activities (Al-Hajaya, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Olohunfunmi \u0026amp; Khairuddin, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Besides, issues of clarity exist in determining apply AI contributions in academic activities and the ownership of information generated (Carobene et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Vasylyshyna et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Furthermore, there is a likelihood of advancing prevailing biases through AI-generated outputs and to create inaccurate outputs due to the use of incorrect training sets (Huang \u0026amp; Huang, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; York et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Thus, people using GenAI should have stringent oversight over what and how they prompt to moderate these risks (Beltran et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Gehrmann et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Making a declaration on the use of AI in academic tasks is an important process for promoting honesty, integrity and academic transparency in academic space (Gulumbe et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; G\u0026uuml;neş \u0026amp; Kaban, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, the lack of comprehensive guidelines on the ethical use and implications of GenAI in academic activities demand a comprehensive and robust user guidelines in applying GenAI tools in academic spaces (An et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Symeou et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eTheoretical Framework\u003c/h3\u003e\n\u003cp\u003ePrimarily, the study dwells on Lawrence Kohlberg\u0026rsquo;s moral development theory, James Rest\u0026rsquo;s ethical reasoning, and Zimmerman\u0026rsquo;s self-regulation theory.\u003c/p\u003e \u003cp\u003eKohlberg\u0026rsquo;s (1969, 1984) conceptualised moral and ethical reasoning through phases: pre-conventional, conventional, and post-conventional stages. At the pre-conventional stage, moral judgments become personal and are made purposely to avoid behavioural consequences (e.g., punishment). In the contexts of cybersecurity, every action of students is primarily guided by acting appropriately with digital information to avoid repercussions and promote positive ethical behaviours. At the conventional stage, actions of students are governed by institutional expectations, societal standards, and complying with established protocols, purposely to maintain harmony. Therefore, students may perceive ethical behaviour in cybersecurity as abiding by the institutional policies, legal frameworks, or professional codes of conduct. At the post-conventional stage, actions of students are fused with abstract thoughts beyond simple imagination. Societal common good for their actions becomes the focal point, where they interpret moral and ethical situations toward general human growth and development. In relating to cybersecurity education, students consider societal consequences when taking cyber actions. Students may make judgement on general gains against societal setbacks. They may engage in responsible technology use even when situations are not clear. Taken together, this model is relevant in cybersecurity education as students are expected to only comply to rules but become principled in their reasoning when challenged by difficult ethical situations such as data privacy, surveillance, and responsible AI use. GenAI-driven learning tasks support this progression by presenting students with novel cyber situations that challenge simplistic, rule-based responses.\u003c/p\u003e \u003cp\u003eSupplementing Kohlberg\u0026rsquo;s work, Rest\u0026rsquo;s (1986) Four-Component Model of morality such as moral sensitivity, moral judgment, moral motivation, and moral character provides a nuanced lens for appreciating ethical actions among people. Specific to this study, GenAI-produced cybersecurity scenarios improve moral understanding by creating clear ethical conflicts, while at the same time insightful tasks support moral judgment and motivation. This viewpoint is predominantly striking in the contexts of cybersecurity, where some decisions that may be unethical affect individuals, communities, and vulnerable populations. Taken together, this theory offers a solid basis for probing how GenAI-driven learning tasks can inure to ethical reasoning and cyber responsibility among students.\u003c/p\u003e \u003cp\u003eIn the case of Zimmerman (2000), self-regulated learning is conceptualised as a recurring process of learning relating to planning, performance monitoring, and self-reflection. Relating to cybersecurity education, students are required to autonomously plan their responses, monitor their reasoning, and evaluate the ethical consequences of their own actions. These processes are supported by GenAI-driven learning situations where students take responsibility in defending their decisions appropriately, adapting to new approaches, and reflect on outcomes in the realm of cybersecurity. Zimmerman\u0026rsquo;s view is supported with a focus on human motivation and metacognitive regulation in the process of learning related ethically complex cyber situations (Purwaningsih, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Tsai et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Taken together, self-regulated learning among students explains that GenAI-enhanced learning environments can promote sustained ethical and responsible cyber behaviours.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eThe Ghanaian Context\u003c/h2\u003e \u003cp\u003eIn light of the challenges presented by using GenAI, stakeholders across academia and other sectors have begun developing guidelines and policies for the ethical deployment of GenAI (Alduais et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Zlotnikova et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). For instance, in Ghana, while there is an increasing interest among educators in the potential use of GenAI, limited research exists that addresses the role of GenAI in the development of ethical reasoning and cyber responsibility among learners (Bannor et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Suglo et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Existing research on AI in education primarily focuses on academic performance, engagement and efficiency of students, but not on the learning outcomes related to ethical education of students (Asamoah \u0026amp; Amarteifio, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Baidoo-Anu et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In addition, there is a growing number of concerns surrounding GenAI in regards to issues related to bias, misinformation, over-dependence upon automated systems and the potential normalization of unethical behaviour attributed to the use of GenAI (Salyer, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Shittu et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Therefore, it is essential that research examines how GenAI will affect not only teaching methods used for ethics, but also assists learners in developing ethical reasoning and a heightened sense of cyber responsibility so that curriculum development, instructional practices and educational policy are aligned with the integration of artificial intelligence within cybersecurity education in the Global South.\u003c/p\u003e \u003cp\u003e \u003cb\u003eResearch Questions\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo what extent do generative AI-driven learning tasks influence students\u0026rsquo; ethical reasoning in digital contexts?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eHow does students\u0026rsquo; engagement with generative AI-driven learning tasks affect students\u0026rsquo; levels of cyber responsibility?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWhat dimensions of ethical reasoning are most strongly associated with participation in generative AI-supported learning activities?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWhat is the influence of students\u0026rsquo; ethical reasoning and their cyber responsibility when learning tasks are mediated by generative AI tools?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Methods and Materials","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eResearch Design and Rationale\u003c/h2\u003e \u003cp\u003eThe study used a quantitative, analytic cross-sectional design. The choice of this methodological procedure was informed by the fact of establishing effects and relationships among the study\u0026rsquo;s constructs (e.g., engagement, ethical reasoning, cyber responsibility) which occur naturally in any learning environment. In line with best practices, the current study stresses associational claims and infers the study results cautiously in terms of causality (Paing, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Practically, the included undergraduate and postgraduate students who pursued ICT courses that employed generative AI tools to support the learning procedures. Specifically, we designed academic activities that relate to generative AI-driven learning tasks in which students applied ChatGPT 4.o to produce initial ideas (e.g., data privacy, academic dishonesty, misinformation, and responsible AI use, misuse of generative AI for assignments), refine their outputs through iterative prompts (e.g., students\u0026rsquo; perspectives of student, teacher, institution, society, data protection, algorithmic bias), judge the worth of their outputs (e.g., ascertaining whether AI-generated recommendations for handling online ethical dilemmas were credible, ethical, and socially responsible, respect for privacy, avoidance of misinformation), and provide justifications for these processes in using the AI tool (e.g., maintaining reflective logs documenting how AI outputs informed their ethical reasoning, explicitly documenting instances where AI-generated solutions were rejected because they conflicted with ethical principles such as honesty, accountability, or respect for others\u0026rsquo; digital rights). In making engagement reflective in the students\u0026rsquo; activities, we explicitly incorporated accountability prompts (e.g., \u0026ldquo;What did you accept, revise, or discard and why?\u0026rdquo;) that allowed students to respond reflectively to components that were related to academic integrity and responsible digital conduct as they use AI tools.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipants and Sampling\u003c/h3\u003e\n\u003cp\u003eThe respondents for this study were 423 (undergraduate\u0026thinsp;=\u0026thinsp;271; postgraduate\u0026thinsp;=\u0026thinsp;153) drawn from diverse year groups of students pursuing academic courses related to ICT. The recruitment process was random and voluntary, where every student in the specified category had the chance to be part and had the freedom to participate or not to participate. The number of respondents recruited for the study was deemed appropriate and adequate for estimating statistical relationships and effects among the various constructs studied. The selection of cases in this study emphasised ecological validity in relation to the study findings because the study constructs were examined in a real instructional setting by recognising that generalizability of the study findings is contextualised to the participating institution. In terms of respondents\u0026rsquo; inclusion eligibility, one must be enrolled as a regular student as of the time of data collection, and one must be taking part in an ICT course that integrates AI in learning tasks. With respect to respondents\u0026rsquo; exclusion eligibility, one must be unwilling to participate, incomplete consent to participate, must be pursuing courses that do not integrate AI tasks, and must be absent from class as of the time of data collection.\u003c/p\u003e\n\u003ch3\u003eMeasures and Operationalisation of Constructs\u003c/h3\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStudents\u0026rsquo; Engagement in AI task\u003c/h2\u003e \u003cp\u003eThe instrument used in measuring students\u0026rsquo; engagement in AI task was a continuous composite from 20-items distributed among three dimensions: behavioural engagement (7-items, α\u0026thinsp;=\u0026thinsp;.75), cognitive engagement (7-items, α\u0026thinsp;=\u0026thinsp;.73), and reflective engagement (6-items, α\u0026thinsp;=\u0026thinsp;.76). All the items on the instrument reflected the extent to which students took part in AI-supported learning activities actively. The instrument was scored on a Likert-type (e.g., SD\u0026thinsp;=\u0026thinsp;1 through to SA\u0026thinsp;=\u0026thinsp;5) and the responses were recorded in that sense. The composite engagement score was calculated as the average of all 20 items. In determining whether the developed scale meets the requirement for the current study, an exploratory factor analysis (EFA) was performed on the 20 items through principal axis factoring (PAF) with Promax rotation (PR). The EFA results showed adequacy of the sampling with KMO= .93, and a significant Bartlett\u0026rsquo;s Test of Sphericity was significant [(\u003cem\u003eχ\u0026sup2;\u003c/em\u003e (190)\u0026thinsp;=\u0026thinsp;4861.42, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001)]. All the three factors extracted produced eigenvalues greater than 1. Again, all the 20 items loaded appropriately and strongly (see Appendix A; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) while the total variance explained was 59.9%.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthical Reasoning in AI Tasks\u003c/h3\u003e\n\u003cp\u003eThe instrument used in assessing ethical reasoning in AI tasks was a conceptualised multidimensional tool that reflected how students used ethical judgment in digital learning situations where AI is embedded. In all, the instrument had 20-items, placed under four dimensions: moral judgment (5-items, α\u0026thinsp;=\u0026thinsp;.78), accountability (5-items, α\u0026thinsp;=\u0026thinsp;.74), fairness (5-items, α\u0026thinsp;=\u0026thinsp;.71), and autonomy (5-items, α\u0026thinsp;=\u0026thinsp;.79). The scale was developed on a five-point Likert-type scale (Strongly Disagree\u0026thinsp;=\u0026thinsp;1 to Strongly Agree\u0026thinsp;=\u0026thinsp;5). The composite of this instrument was calculated from all the 20-items as an average of all the four dimensions. Furthermore, an EFA was performed on the 20 items under scale and it produced four-factor solution indicating Moral judgment, accountability, fairness, and autonomy. The KMO value was .91, while Bartlett\u0026rsquo;s Test was significant [\u003cem\u003eχ\u0026sup2; (\u003c/em\u003e190)\u0026thinsp;=\u0026thinsp;5124.36, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001)]. Again, the items under the scale loaded strongly (see Appendix A; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), while the total variance explained was 61.3%.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eCyber Responsibility in AI Tasks\u003c/h3\u003e\n\u003cp\u003eIn this study, cyber responsibility was conceptualised as the way students conduct themselves responsibly and their decision-making procedures in technology-mediated learning situations where generative AI tools are used. The instrument used to assess the cyber responsibility was a 20-item inventory, distributed among three dimensions: responsible data use (7-items, α\u0026thinsp;=\u0026thinsp;.77), academic integrity (7-items, α\u0026thinsp;=\u0026thinsp;.75), and online conduct (6-items, α\u0026thinsp;=\u0026thinsp;.79). The scale was developed on a five-point Likert-type scale (Strongly Disagree\u0026thinsp;=\u0026thinsp;1 to Strongly Agree\u0026thinsp;=\u0026thinsp;5). The composite of this instrument was calculated from all the 20-items as an average of all the four dimensions. Aside from these, an EFA was performed on the 20 items and it showed a three-factor structure. The results of this process produced a KMO of .92, and Bartlett\u0026rsquo;s Test of [(\u003cem\u003eχ\u0026sup2; (\u003c/em\u003e190)\u0026thinsp;=\u0026thinsp;4978.09, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001)]. In all, the items loaded appropriately and strongly (see Appendix A; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) while the variance explained was 57.1%.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003ePrimarily, the data gathered were screened and cleaned to ensure accuracy, quality, distributional properties, and appropriateness before the analyses. to assess data. Again, EFAs were conducted on all the constructs because their items were new and it was appropriate to establish latent structure and construct validity for all using Partial Least Squares Structural Equation Modelling [PLS-SEM] (Zia et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Lastly, regression analyses were performed in relation to each question at a statistical significance threshold of .05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe study explored the degree to which generative AI-driven learning tasks influence ethical reasoning in academic and digital contexts of students. The data for this study were quantitative, where descriptive (e.g., means and standard deviations) and inferential (e.g., multiple linear regression) statistical analyses were performed using SPSS v23. The analyses and presentation of the results were based on the research question proposed for the study.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eResearch Question One\u003c/strong\u003e \u003cp\u003e \u003cem\u003eTo what extent do generative AI-driven learning tasks influence students\u0026rsquo; ethical reasoning in academic and digital contexts?\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe descriptive results showed a moderate to high level of exposure to generative AI-driven learning tasks among students (M\u0026thinsp;=\u0026thinsp;3.61, SD\u0026thinsp;=\u0026thinsp;0.72) while ethical reasoning in academic and digital contexts by students showed a high level (M\u0026thinsp;=\u0026thinsp;3.74, SD\u0026thinsp;=\u0026thinsp;0.68). The descriptive results were further expatiated with the regression results, where statistically significant results were established for the regression model [\u003cem\u003eF\u003c/em\u003e (1, 421)\u0026thinsp;=\u0026thinsp;97.84, p \u0026lt; .001]. The results showed that generative AI-driven learning tasks significantly predicted ethical reasoning (B\u0026thinsp;=\u0026thinsp;0.41, SE\u0026thinsp;=\u0026thinsp;0.04, β\u0026thinsp;=\u0026thinsp;.43, p \u0026lt; .001) with 17% of the variance in ethical reasoning explained by generative AI tasks. By implication, an increased engagement with AI-driven learning tasks among students associate with higher levels of ethical reasoning in digital learning contexts. These can be found in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eMultiple Linear Regression Predicting Ethical Reasoning from Generative AI-Driven Learning Tasks\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenerative AI-Driven Learning Tasks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eNote\u003c/b\u003e: N\u0026thinsp;=\u0026thinsp;423; R\u0026sup2; = .17; B\u0026thinsp;=\u0026thinsp;unstandardized coefficient; β\u0026thinsp;=\u0026thinsp;standardized coefficient\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eResearch Question Two\u003c/strong\u003e \u003cp\u003e \u003cem\u003eTo what extent does engagement with generative AI-driven learning tasks affect students\u0026rsquo; levels of cyber responsibility?\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eDescriptive Statistics for Engagement and Cyber Responsibility Outcomes\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEngagement AI Tasks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCyber Responsibility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResponsible Data Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcademic Integrity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnline Conduct\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe descriptive results in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e showed a moderate engagement with AI-driven learning tasks by students (M\u0026thinsp;=\u0026thinsp;3.42, SD\u0026thinsp;=\u0026thinsp;0.63) while cyber responsibility a high level (M\u0026thinsp;=\u0026thinsp;3.23, SD\u0026thinsp;=\u0026thinsp;0.44).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003ePearson Correlations Between Engagement and Cyber Responsibility Outcomes\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCyber Responsibility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResponsible Data Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcademic Integrity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnline Conduct\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the results showed that students\u0026rsquo; engagement with AI-driven learning tasks related positively with overall cyber responsibility (r = .713, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). At the dimension level, students\u0026rsquo; engagement with AI-driven learning tasks related positively with responsible data use (r = .496, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), academic integrity (r = .575, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), and online conduct (r = .425, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). Jointly, these bivariate relationships showed that when students\u0026rsquo; engagement with AI-driven learning tasks increases, their cyber responsibility equally increases.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eOLS Regression Predicting Cyber Responsibility Composite from Engagement With AI-Driven Tasks Composite\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95% CI [LL, UL]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEngagement AI Tasks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[.448, .542]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender Male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[-.135, \u0026minus;\u0026thinsp;.016]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior AI Training\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[-.015, .107]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFurthering the analyses to ascertain the exclusive effect of students\u0026rsquo; engagement with AI-driven learning tasks on cyber responsibility while taking into consideration covariates, we performed an Ordinary Least Square (OLS) regression model by fitting the composite score of cyber responsibility as a criterion variable and using engagement, sex of students, year of study, and prior AI training as explanatory variables in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. From these analyses, students\u0026rsquo; engagement with AI-driven learning tasks significantly predicted their cyber responsibility [B = .495, β\u0026thinsp;=\u0026thinsp;.705, SE = .024, \u003cem\u003et\u003c/em\u003e (418)\u0026thinsp;=\u0026thinsp;20.81, p \u0026lt; .001, 95% CI [0.448, 0.542]. With this significant model, 52.5% of the variance in cyber responsibility was explained by students\u0026rsquo; engagement with AI-driven learning tasks. However, sex (coded 1\u0026thinsp;=\u0026thinsp;male) indicated a small significant but an inverse relationship with cyber responsibility (B\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.076, \u003cem\u003ep\u003c/em\u003e = .013, 95% CI [-.135, \u0026minus;\u0026thinsp;.016]), while prior AI training was not significant statistically significant (B=.046, \u003cem\u003ep\u003c/em\u003e = .140, 95% CI [-.015, .107]). This pattern suggests that engagement with AI-driven learning tasks is the dominant predictor of responsible cyber-related attitudes and behaviours in this dataset, above and beyond basic demographic and variations in exposure.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eResearch Question Three\u003c/strong\u003e \u003cp\u003e \u003cem\u003eWhat dimensions of ethical reasoning are most strongly related to participation in generative AI-supported learning activities?\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003eIn responding to the question, we performed Pearson Product\u0026ndash;Moment (PPM) correlation and multiple linear regression at .05 significant level. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the results.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eCorrelations and Regression Results for Ethical Reasoning Dimensions Predicting Generative AI Engagement\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoral Judgment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccountability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFairness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAutonomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the results showed a statistically significant positive relationships between students\u0026rsquo; engagement in generative AI-supported learning activities and the dimensions of ethical reasoning. For instance, accountability had the highest relationship (r = .55, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), followed by moral judgment (r = .48, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), fairness (r = .42, \u003cem\u003ep\u003c/em\u003e = .002), and autonomy (r = .29, p = .041). Furthermore, the regression model was significant [\u003cem\u003eF\u003c/em\u003e (4, 418)\u0026thinsp;=\u0026thinsp;72.64, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001)] and the results showed that the dimensions of ethical reasoning together accounted for 41% of the variance in students\u0026rsquo; engagement with generative AI-supported learning activities. Specifically, the accountability dimension was the highest predictor (β\u0026thinsp;=\u0026thinsp;.38, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), followed by moral judgment dimension (β\u0026thinsp;=\u0026thinsp;.31, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), then the fairness dimension (β\u0026thinsp;=\u0026thinsp;.21, \u003cem\u003ep\u003c/em\u003e = .002), and the autonomy dimension (β\u0026thinsp;=\u0026thinsp;.12, \u003cem\u003ep\u003c/em\u003e = .041).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eResearch Question Four\u003c/strong\u003e \u003cp\u003e \u003cem\u003eWhat is the relationship between students\u0026rsquo; ethical reasoning and their cyber responsibility when learning tasks are mediated by generative AI tools?\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003eIn answering this question, the data were tested using Pearson Product\u0026ndash;Moment (PPM) correlation and Simple Linear Regression (SLR). Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents the results.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eCorrelation and Simple Linear Regression Predicting Cyber Responsibility from Ethical Reasoning\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE B\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAdj. R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthical Reasoning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e61.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePearson correlation analysis revealed a statistically significant, positive relationship between ethical reasoning and cyber responsibility, r\u0026thinsp;=\u0026thinsp;0.54, p \u0026lt; .001. This indicates that students who demonstrated higher levels of ethical reasoning also tended to report higher levels of cyber responsibility when engaging with generative AI\u0026ndash;supported learning tasks. The simple linear regression [\u003cem\u003eF\u003c/em\u003e (1, 148)\u0026thinsp;=\u0026thinsp;61.78, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001] further showed that ethical reasoning significantly predicted cyber responsibility, β\u0026thinsp;=\u0026thinsp;0.55, p \u0026lt; .001, explaining approximately 29.3% of the variance in cyber responsibility (R\u0026sup2; = 0.29). This suggests that ethical reasoning is a substantive cognitive\u0026ndash;moral factor underpinning responsible digital behaviour in AI-mediated learning environments.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examined how engagement with generative artificial intelligence (GenAI)\u0026ndash;driven learning tasks shapes students\u0026rsquo; ethical reasoning and cyber responsibility in academic and digital contexts. Taking into consideration theoretical perspectives such as moral development and self-regulated, the study findings offer strong practical evidence that GenAI can work as a powerful instructive catalyst for ethical thinking and accountable digital conduct. Specifically, this revelation aligns with the post-conventional stage of moral reasoning proposed by Kohlberg, as students may judge their actions in relation to values and standards establish by the broader institutional community. Significantly, the findings illuminate discourse on GenAI beyond performance and efficiency outcomes by dwelling on ethical development and cyber responsibility (Bannor et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Suglo et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSimilarly, the study showed that students\u0026rsquo; ethical reasoning abilities were influenced by their engagement with GenAI-driven learning tasks in the era of digital technologies. This finding corroborates the evolving arguments that GenAI tools are not just tools for productivity but a moral learning environment possibly to evoke reflective judgment, ethical awareness, and principled decision-making among students\u0026rsquo; users (Alduais et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Đerić et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Importantly, GenAI-driven activities commonly present vague, poor-structured problems that require students to extend their reasoning abilities and engage in abstract ethical judgement (Zohny et al., 2023; Lin, 2023).\u003c/p\u003e \u003cp\u003eAgain, the study revealed a significant direct positive association between students\u0026rsquo; engagement with GenAI-driven learning tasks and their cyber responsibility. Surprisingly, this is noteworthy, as it challenges predominant traditions that the higher one uses AI, the higher they are tempted to become dishonest in their use through ethical erosion (Al-Hajaya, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; York et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). By implication, the findings suggest that the ways students engage with GenAI is important than whether they engage at all in the learning setting, which points to self-regulatory abilities of students. For instance, tasks in GenAI demand planning on prompts, outputs, evaluation, and making a decision. Such procedures reflect the foresight, effort, and self-reflection phases of self-regulation (Zimmerman, 2000). Empirically, students with higher self-regulatory abilities show higher ethical awareness and cyber responsibility because they actively evaluate consequences and adjust behaviour accordingly (Purwaningsih, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Tsai et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, the study found that all dimensions of students\u0026rsquo; ethical reasoning in AI use significantly related positively with their participation in GenAI-supported learning activities, where accountability became the highest predictor. This revelation possessed theoretically and practically connotation. For example, accountability in itself brings about ownership to decisions made or taken by students, and are always willing to take responsibility for such decisions (Carobene et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Vasylyshyna et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Therefore, when students record high scores in accountability, they may use GenAI meaningfully and responsibly because every action in that sense becomes a choice. This aligns with previous studies indicating that a clear reflection on responsibility in the use of AI reduces misconduct in the use of AI (Beltran et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Gulumbe et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLastly, the revealed that ethical reasoning predicted cyber responsibility strongly and significantly. This outcome affirms the assertion that ethical reasoning in AI use serves as a basic cognitive\u0026ndash;moral mechanism supporting responsible digital behaviours among students. The finding aligns with current cybersecurity ethics scholarship, which contends that methodological precautions are insufficient in guiding appropriate cyber behaviours among students without a concerted ethical judgment protocol towards responsible user behaviour (Branham, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Mahmood et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Literary, students who can reason ethically are better positioned to navigate dilemmas involving data privacy, misinformation, social engineering, and AI misuse (Olohunfunmi \u0026amp; Khairuddin, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Takgil, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eBased on the study findings, it is concluded that GenAI can meaningfully contribute to students\u0026rsquo; ethical development educational curricula because GenAI-supported learning environments can serve as ethical learning spaces for students in Ghana. Again, GenAI-driven learning tasks primarily influence how students\u0026rsquo; reason ethically and behave responsibly in digital space and not about reshaping they learn. As GenAI is purposely integrated in global education systems, such intuitions may provide a compelling evidence base for reimagining AI as moral learning opportunity and not as a moral risk.\u003c/p\u003e \u003cp\u003e \u003cb\u003eRecommendations for Policy and Practice\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eHigher education institutions should develop clear, context-sensitive policies governing the ethical use of generative AI in teaching, learning, and assessment. These policies should move beyond prohibition and instead emphasize responsible use, transparency, accountability, and ethical reasoning as core learning outcomes.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eManagers of HEIs in collaboration with the Ministry of Education and Ghana Education Service, should explicitly embed ethical reasoning, cyber responsibility, and AI literacy into higher education curriculum frameworks. This is particularly important in the Global South, where policy gaps may exacerbate misuse or inequitable adoption of AI technologies.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eFaculty should design GenAI-supported tasks that explicitly require students to justify AI use, evaluate ethical implications, and reflect on decision-making processes. Such tasks strengthen accountability, moral judgment, and self-regulation.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eHEIs should provide ongoing professional development for lecturers and instructional designers focused on ethical AI pedagogy, including how to facilitate discussions on bias, privacy, fairness, and responsible AI use.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eContextual Implication\u003c/h2\u003e \u003cp\u003eThe integration GenAI in education can help promote ethical capacity building and reduces the apprehension of its threat to academic integrity. However, the findings of this study are contextually salient in Ghana. Although Scholars in Ghana have begun to embraces GenAI in their activities, ethical frameworks and institutional guidelines remain blurry (Bannor et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Baidoo-Anu et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Suglo et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSuggestions for Further Research\u003c/h2\u003e \u003cp\u003eIn furthering this area, experimental studies comparing different types of GenAI-driven learning tasks (e.g., reflective vs. non-reflective designs) would provide stronger causal evidence regarding which pedagogical approaches most effectively promote ethical outcomes.\u003c/p\u003e \u003cp\u003eAgain, comparative studies across countries and regions within the African Continent would deepen understanding of how cultural, institutional, and policy contexts mediate the relationship between GenAI engagement, ethical reasoning, and cyber responsibility.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with established ethical standards for research involving human participants. Ethical approval for the study was obtained from the Institutional Review Board (IRB) of the University of Education, Winneba prior to data collection (UEW-IRB-01-25). The research procedures complied with institutional guidelines and the principles outlined in the Declaration of Helsinki. Participation in the study was voluntary. Before completing the survey, participants were provided with a detailed information sheet explaining the purpose of the study, the nature of their involvement, potential risks and benefits, confidentiality assurances, and their right to withdraw at any time without penalty. Informed consent was obtained electronically from all participants before they were allowed to proceed with the questionnaire. No personally identifiable information was collected. Responses were anonymized at the point of data collection, and data were stored securely on password-protected devices accessible only to the research team. The study posed minimal risk to participants, as it involved self-reported perceptions related to engagement with generative AI-driven learning tasks, ethical reasoning, and cyber responsibility in academic contexts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe appreciate all collaborators for their effort in this study. Above all, we thank our participants for availing themselves for this survey.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data can be given out when a formal and an ethical request is made to the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interest statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe have no competing interests to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration on the use of Generative AI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe researchers employed the services of ChatGPT and Quill Bot in checking grammar and flow of ideas in the write-up.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external funding. The study was conducted as part of the authors\u0026rsquo; academic and institutional research activities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIM conceptualized the study, developed the theoretical and analytical framework, designed the methodology, conducted the statistical analyses, and drafted the original manuscript. He also coordinated the overall research process. JOE contributed to the refinement of the research design, validated the analytical procedures, and critically reviewed the manuscript for intellectual rigour. RK supported data collection and management, contributed to literature synthesis, and participated in manuscript revision. MS contributed to the theoretical framing of the study, provided high-level academic supervision, and critically revised the manuscript. SOD assisted with statistical interpretation, supported data presentation and visualization, and reviewed the manuscript. PE provided strategic oversight, contributed to the intellectual direction of the study, and critically revised the manuscript for important scholarly content.\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlduais A, Qadhi S, Chaaban Y, Khraisheh M. Utilizing Generative AI responsibly and ethically for research purposes in higher education: A policy analysis. 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J Inform Communication Ethics Soc. 2025;23(2):188\u0026ndash;216.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Generative artificial intelligence, Ethical reasoning, Cyber responsibility, AI-mediated learning, Higher education","lastPublishedDoi":"10.21203/rs.3.rs-8855662/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8855662/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe rapid integration of generative artificial intelligence (GenAI) into higher education has heightened ethical concerns related to academic integrity, responsible technology use, and cybersecurity. Despite growing interest in GenAI, empirical evidence on its role in shaping students\u0026rsquo; ethical reasoning and cyber responsibility remains limited. Therefore, this study investigated the problem using a quantitative cross-sectional design, where data were collected from 423 undergraduate and postgraduate students enrolled in ICT-related courses. Three newly developed instruments: the Engagement with Generative AI\u0026ndash;Driven Learning Tasks Scale, the Ethical Reasoning in Academic and Digital Contexts Scale, and the Cyber Responsibility in AI-Mediated Learning Scale developed and validated through exploratory factor analysis while descriptive statistics, Pearson correlations, and regression analyses were employed used. Results showed that engagement significantly predicted ethical reasoning, accounting for 17% of its variance, and strongly predicted cyber responsibility, explaining over 52% of the variance, while accountability emerged as the strongest ethical reasoning dimension associated with GenAI engagement. Furthermore, ethical reasoning predicted cyber responsibility. The findings suggest that well-designed GenAI-driven learning tasks can promote ethical reasoning and cyber responsibility. Implications are discussed for curriculum design, pedagogy, and policy development in AI-integrated higher education, particularly in the Global South.\u003c/p\u003e","manuscriptTitle":"Developing Students’ Ethical Reasoning and Cyber Responsibility through Generative AI- Driven Learning Tasks","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-24 16:01:27","doi":"10.21203/rs.3.rs-8855662/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3978c2ad-2f46-4fd2-9507-098df86f4c7d","owner":[],"postedDate":"February 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-23T03:54:47+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-24 16:01:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8855662","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8855662","identity":"rs-8855662","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}