The Role of Artificial Intelligence in Enhancing Human Longevity: Mitigating Cognitive Overload for Extended Lifespan Among Master’s Students in Mathematics at the Catholic University of Ghana

The Role of Artificial Intelligence in Enhancing Human Longevity: Mitigating Cognitive Overload for Extended Lifespan Among Master’s Students in Mathematics at the Catholic University of Ghana

Richmond Kakra Addae, Cecilia Brown

Faculty of Education, Catholic University of Ghana, Fiapre

DOI: https://dx.doi.org/10.47772/IJRISS.2025.905000236

Received: 23 April 2025; Accepted: 05 May 2025; Published: 09 June 2025

ABSTRACT

This study addresses the escalating concern of academic stress among students, specifically investigating its impact on their overall well-being and performance. Employing a mixed-methods approach, the research integrates quantitative surveys and qualitative interviews to examine stressors, coping mechanisms, and psychological consequences. The objective is to uncover the relationship between perceived academic pressure and indicators of mental health such as anxiety and burnout. Key findings reveal significant correlations between stress levels and both academic outcomes and emotional resilience, highlighting the urgent need for institutional interventions.

The study is guided by Cognitive Load Theory (Sweller, 1988), Allostatic Load Theory (McEwen, 1998), and Socio-Technical Systems Theory (Trist, 1981) and follows a mixed-method approach consisting of survey data from 100 master’s students and in-depth interviews. The survey revealed that 78 percent of the respondents were moderately to highly cognitively overloaded, while 65 percent utilized AI to work on their academic tasks. A notable negative relationship was found between AI use and perceived stress (r = -0.42, p < 0.01), with more confidence in academic work and improved time management. ANOVA revealed significant differences in stress levels among the different frequencies of AI use (F = 4.56, p < 0.05).

Qualitative analyses generated the core themes of “AI as a cognitive assistant,” “digital dependency,” and “emotional assurance.” Their reports indicated that they used AI to help them understand complex mathematical ideas, organize their academic writing, and calm their nerves about presentations. However, they voiced worries about becoming overdependent on it, issues of academic integrity, and ethics concerning AI’s role of AI in learning.

The end product maintains that AI is a reasonable tool for easing cognitive load, encouraging mental well-being, and enhancing cognitive longevity. These findings have policy implications for the inclusion of AI literacy in both academic and national digital educational frameworks. AI implementation strategies could disappear into the topping of teaching success while serving the broader public health agenda of student mental health and human capital development.

Keywords AI, cognitive overload, cognitive longevity, academic stress, higher education, AI literacy, public health, mathematics education, mental well-being, Ghana

INTRODUCTION

AI has become a popular tool for improving productivity, efficiency, and decision- making in various fields such as education and healthcare (Russell & Norvig, 2016). While much attention has been paid to the practical use of AI for health diagnostics, robotics, and predictive statistics, the psychological and cognitive benefits of AI,

particularly in an academic setting, have received little attention. Cognitive overload among students needs to be understood immediately because cognitive strain affects not only academic performance, but also the long-term well-being of students (Sweller, Ayres, & Kalyuga, 2011).

Postgraduate mathematics students usually face the challenge of working with abstract concepts, which also places a high demand upon their mental faculties and hence their worthiness for stress and cognitive fatigue (Paas, Renkl, & Sweller, 2003). Limited mental health support services in academic institutions in Ghana have aggravated this problem (Amponsah & Owolabi, 2011). An innovative intervention to relieve cognitive stress and build resilience is the use of AI as a cognitive theoretical tool in terms of adaptive learning systems, intelligent tutoring, and instant feedback facilities. Therefore, the present study examines the use of AI to alleviate cognitive overload and its long-term implications for longevity among master’s mathematics students at the Catholic University of Ghana.

Problem Statement

The high cognitive pressure exerted on master’s students in mathematics renders them mentally fatigued, academically impaired, and psychologically distressed in the long term. While the advent of educational systems using artificial intelligence tools is in progress, there is still an insufficient amount of research on the role of AI in alleviating cognitive overload and enhancing health resilience within the academic setup. In particular, studies that demonstrate varying contextual evidence from Ghanaian institutions regarding the influence of AI on student mental health and longevity are rare. This gap was filled by assessing the role of AI in mental stress management and in promoting well-being among postgraduate mathematics students in Ghana.

Research Objectives

This research focuses on how Artificial Intelligence (AI) might help master’s students studying mathematics at the Catholic University of Ghana live longer. It explores how AI can lower mental stress and make it easier for students to handle a large amount of information. By reducing this stress, AI could contribute to improving the overall well-being and lifespan.

General Objective:

To examine how an AI tool can work to lessen the mental fatigue and stress of students pursuing a postgraduate degree in mathematics. Thus, less cognitive overload can improve academic performance and the degree of well-being. Ultimately, these improvements might account for the students’ longer than average lifespan.

Specific Objectives:

• To investigate the magnitude of cognitive overload faced by master’s students in mathematics at the Catholic University of Ghana.
• To assess the different types, functionality, and effectiveness of AI tools, students used them to alleviate their academic workload.
• To explore the perceived influence of AI implementation on students’ stress levels and cognitive efficiency.
• To explore the relationship between diminished cognitive load through AI and the mental well-being of students and their long-term health.
• To draw on theoretical underpinnings from Cognitive Load Theory, Allostatic Load Theory, and Socio-Technical Systems Theory to argue the relationship between AI application, reduction of academic stress, and enhanced longevity.
• To come up with suggestions for institutional policy and teaching approaches to stimulate AI application for reducing stress and enhancing health

Theoretical and Conceptual Frameworks

Cognitive Load Theory: According to Sweller’s (1988) Cognitive Load Theory, working memory has a limited

capacity, and instructional design should relieve extraneous cognitive load while adding germane load. AI-powered educational platforms such as adaptive quizzes and real-time feedback tutors can free students from normal demands on cognitive processing so that they can devote more attention to important learning.

Allostatic Load Theory: McEwen and Stellar (1993) described how chronic stresses that lead to physiological changes, such as higher cortisol levels and the corresponding threats to the health of many individuals, may occur over time. Students with chronic cognitive stress are predisposed to a cumulative allostatic load. AI makes learning automatic and easier as a means of buffering students against ill effects, thereby improving their well-being and longevity.

Sociotechnical Systems Theory: This theory was introduced by Trist and Bamforth (1951) to examine the co-evolution of human and technological systems in organizational environments. Thus, it is important to recognize that such AI tools are context-sensitive to appropriateness in adoption by users, institutional structures for support, and collaboration dynamics

Figure 6: Conceptual diagram illustrating how AI mediates the relationship between cognitive load (Sweller), stress, physiological response (McEwen), and the socio-technical academic system (Trist).

Philosophical Assumptions

This study is founded on several philosophical assumptions that guide the inquiry, methodology, and interpretation of the findings. These include ontological, epistemological, axiological, and methodological assumptions, all of which are linked to the nature of the study and its objectives.

Ontological Assumption

Ontology deals with the nature of reality. This study adopts a critical realist stance, recognizing that reality exists independently but is interpreted through human experience (Bhaskar, 1978). Cognitive overload, stress responses, and socio-technical conditions that touch on Ai adoption in the academic setting are indeed real-world phenomena; the lived experiences of students, educators, and institutional actors will continue to mediate such realities so that qualitative and interpretative methods are essential.

Ontology reveals the nature of reality. This study takes a critical realist stance, claiming that reality exists outside the human mind but can only be understood through human perception and interpretation (Bhaskar, 1978). Such factors are examined as real-world phenomena interpreted through the lived experiences of students, educators, and institutional actors, warranting the use of qualitative and interpretative methods.

Epistemological Imagination

Epistemology addresses the nature of knowledge and the methods of acquiring knowledge. In this study, constructivist epistemology treats knowledge as being co-constructed socially and through context (Lincoln & Guba, 1985). Given that it concerns the use of AI in the alleviation of cognitive overload in a particular audience–more specifically, master’s students in mathematics at the Catholic University of Ghana–it focuses on experiential knowledge gained through surveys and interviews. This is consistent with the interpretivist paradigm that prioritizes subjective meaning over objective measures.

Axiological Assumption

The values in this study essentially form the axiology domain. In this study, the researcher’s values were recognized to inform the research process along with those of the participants. As for the decision to investigate the potential role of AI in improving student mental well-being and reducing academic stress, there is a value-oriented sense attached to it regarding normative concerns about the nature of well-being, equity, and scholarly support. Transparency, compassion, and ethical integrity mark the process of data collection and analysis associated with this research, whereas other important considerations include informed consent, confidentiality, and respect for divergent views in the study design.

Methodological Assumption

Allegorically, this study maintained a mixed-methods philosophy, realizing quantitative surveys complemented by qualitative insights obtained from interviews. This process of pluralism finds regimentation under the aegis of pragmatism (Creswell and Clark 2011). Pragmatism emphasizes issue solving and sensitivity to context rather than a set way of doing things. Such a philosophy allows the study to probe not only the quantifiable cognitive and stress-related outcomes but also subjective viewpoints concerning AI tools in the hands of graduate students and their individual concrete experiences.

Rationale for the Philosophical Approach

This integrated philosophical framework provides an in-depth understanding of how a problem space can be construed or interrogated in multiple dimensions. Thus, reference to this study as critical realism and pragmatism enables theoretical inquiry and applied solutions such as institutional policy and technological implementation. By encouraging constructivism, it honors the voice and agency of the participant, while the axiological perspective encloses the study’s ethical foundation. All of these philosophical commitments are imperative to such real and complex issues as educational stress and health outcomes during the digital age. Together, these assumptions will orient research toward understanding how AI harbors interventions concerning students’ cognitive experiences within academic environments as well as the broader systemic vulnerabilities they contend with.

LITERATURE REVIEW

Cognitive overload remains a core challenge for academic environments, especially in high-demand subjects like mathematics that are abstract and require manipulations over multiple steps (Sweller, 1988; Paas, Renkl, & Sweller, 2003). As the learner’s working memory capacity is taken away by increasing instigated cognitive demand, academic activity gets displaced, while psychological distress and disengagement get set in (Kirschner, 2002; Van Merriënboer & Sweller, 2005). In Ghana, however, this is made worse by infrastructural deficiencies in the educational sector, which translates to poor learning environments, further limiting the learners’ ability to engage with, process, and place down academic content (Amponsah & Owolabi, 2011).

AI is offering a plausible countermeasure to cognitive overload by offering more targeted help to learners via intelligent tutoring systems (ITS), adaptive quizzes, and AI-powered dashboards. In essence, these instruments reduce extraneous cognitive load and promote germane processing in line with Sweller’s instructional design principles (Sweller, 1988; Roll & Wylie, 2016; Luckin et al., 2016). Other studies have found that AI-based mechanisms for giving feedback help students retain information and regulate their emotions while doing difficult tasks as a result of reducing trial-and-error learning adverse effects (Holmes et al., 2019). Yet, for an AI to integrate well into an educational system, it must be more than just a technology solution; it must incorporate the readiness of institutions, acceptance by users, and a strong digital infrastructure (Yang & Evans, 2019; Selwyn, 2019).

Beyond academic outcomes, cognitive stress exerts its effects on the health and the length of life. In this sense, the concept of allostatic load introduced by McEwen and Stellar stresses that academic stress maintained over time interferes with physiological equilibrium, causing chronic inflammation, hormonal dysfunctions, and cognitive disruptions (Lupien et al., 2009; Sapolsky, 2004). There is evidence that with decreased mental workload through relaxing activities or through supportive interventions using AI, the development of stress-related diseases can be delayed, and life expectancy can be increased (Fries, 2002; Olshansky et al., 2018). This means that AI has an important role in promoting health, especially in situations requiring intense cognitive work in educational fields, for instance, graduate studies.

Yet, the adoption and scalability of AI-ed must take into account the social-technical perspective. The educative innovation does not live in a vacuum; it grows and evolves alongside human agency and technological infrastructure. From a socio-technical systems (STS) perspective (Trist & Bamforth, 1951), a number of scholars support the argument that there should exist some form of participative design involving educators and learners in implementing such AI tools (Baxter & Sommerville, 2011). Furthermore, collaboration in pedagogy, practice feedback through evidence, and sensitivity towards local cultural contexts are at the heart of successful AI deployments (Demetriadis et al., 2011). Issues of equity scoot to the foreground, especially concerning the digital divide and differences in access to AI-facilitated learning platforms (Selwyn, 2019).

In summary, many experts believe that using AI to help manage cognitive overload is more than just a new teaching strategy; it’s also crucial for public health and ensuring fairness. In countries like Ghana, it is essential to adapt these international ideas to match the local culture and education systems. This requires detailed research that takes into account the complex experiences of learners. It also supports the effective use of AI in schools that might not have a lot of resources.

METHODOLOGY

It is a methodological framework under which studies would be conducted on the use of Artificial Intelligence (AI) to alleviate cognitive overload among master’s students in mathematics at the Catholic University of Ghana and contribute towards improving well-being and longevity. This study utilized a mixed-method approach because it accommodates both breadth and depth in an inquiry into the objectives set out in this research.

Research Design

Using a convergent parallel mixed methods design (Creswell & Plano Clark, 2011), the data were collected in a quantitative and qualitative manner at the same time and were analyzed separately before being merged for joint interpretation. Cross-validation of the findings affords a more sophisticated understanding of the varied and complex interactions between AI use, cognitive load, and stress responses.

Study Setting and Participants

Research conducted at the Catholic University of Ghana is a tertiary institution with a fine academic tradition and increasing investments in digital learning tools. The emphasis of the study is on master’s students enrolled in mathematics programs; these are particularly relevant because their subject area derives much from the cognitive costs of the content.

Sampling Procedures

A stratified random sampling technique was used to select participants for the quantitative component, ensuring representation across different levels of the study and by gender. For the qualitative component, purposive sampling was adopted to capture a range of experiences with AI. The sample size for the survey was 100 students; 12 students were selected for in-depth interviews.

Data Collection Instruments

• Survey Questionnaire: The instrument was structured around four domains: cognitive load, AI usage frequency and type, academic stress, and academic outcomes. It integrates validated tools such as the NASA Task Load Index (NASA-TLX) for cognitive workload (Hart & Staveland, 1988) and the Perceived Stress Scale (PSS) (Cohen et al., 1983).
• Semi-Structured Interview Guide: This includes open-ended questions designed to elicit narratives about students’ interactions with AI tools, perceived mental and emotional benefits, challenges, and implications for long-term well-being. The interviews allowed for the exploration of deeper sociotechnical and health-related dimensions.

Instrument Validity and Reliability To ensure content validity, instruments are reviewed by subject experts in educational psychology and digital pedagogy. The construct validity was verified through pilot and correlation analyses. Reliability was measured using Cronbach’s alpha, with a threshold of α ≥ 0.70 indicating internal consistency. Qualitative trustworthiness was ensured through triangulation, member checking, and audit trials.

Data Analysis Procedures

• Quantitative data were analyzed using SPSS (version 26). Descriptive statistics (mean, standard deviation, and frequency distribution) describe the student demographics and AI usage patterns. Inferential statistics, including Pearson’s correlation, linear regression, and ANOVA, were used to explore the associations among the variables.
• Qualitative Analysis: Interview transcripts were analyzed thematically using NVivo software. Codes were derived both deductively (based on theoretical frameworks) and inductively (emerging from participants’ responses). Themes were mapped onto a conceptual model linking AI, cognitive load, stress, and health.

Ethical Considerations Ethical approval was obtained from the Catholic University of Ghana Research Ethics Committee. Participants received detailed information sheets and signed informed consent forms. Data confidentiality was ensured by anonymizing the responses and by securely storing the datasets. Participants were informed of their right to withdraw from the study at any time.

Limitations The study is limited by its focus on a single academic institution and discipline, which may affect generalizability. Furthermore, the reliance on self-reported data introduces a potential response bias. However, the mixed-methods design enhanced internal validity by corroborating the findings across data sources.

Delimitations The scope of this research is restricted to AI applications in cognitive and stress mitigation within the academic domain. It does not explore the broader implications of AI in society or unrelated academic fields.

This rigorous methodological design ensured that the study’s findings offered robust and contextually relevant insights into the integration of AI in academic stress management and its broader implications for human longevity.

RESULTS AND DISCUSSION

This section presents the integrated findings of the quantitative and qualitative data, followed by a theoretical discussion based on the Cognitive Load Theory (Sweller, 1988), Allostatic Load Theory (McEwen & Stellar, 1993), and Socio-Technical Systems Theory (Trist & Emery, 1960). Convergence of data enhances the credibility and depth of interpretation.

Demographic Analysis of Respondents

The survey included 100 Master’s students in mathematics from the Catholic University of Ghana. A comprehensive analysis of their demographic profiles provided context for interpreting AI tool usage patterns and perceived cognitive load.

Demographic Tables and Charts

Table 1: Age Distribution of Respondents

Figure 2: Bar chart showing Age Distribution

The largest age group was 23–27 years, comprising 56% of respondents, followed by 28–32 years (30%), and those above 32 years (14%). This age structure reflects a typical postgraduate cohort that balances academic pursuits with early professional responsibilities.

Table 2: Gender Representation of Respondents

Figure 3: Pie chart showing Gender Representation

Of the participants, 60% were male, 3 were female, and 2% were identified as others or preferred not to disclose. This aligns with broader trends of male predominance in mathematical sciences, although female participation appears substantial.

Program and years of study

Table 4: Program and Year of Study

Figure 4: Grouped bar chart showing Program and Year of Study

All respondents were enrolled in master’s mathematics programs, with 70% specializing in Pure Mathematics and 30% in Applied Mathematics. Additionally, 52% were in their first year, whereas 48% were in their second year, indicating nearly equal distribution across the study duration.

Internet Access and AI Tool Usage Frequency

Table 3: Frequency of AI Tool Usage Among Students

Figure 5: Bar chart showing AI Tool Usage Frequency

88% of the students reported consistent Internet access at home, which is critical for leveraging AI-based learning tools. Regarding AI usage frequency, 40% used AI tools daily, 25% weekly, 20% monthly, and 15% rarely or never. This widespread AI engagement sets the foundation for subsequent analysis of cognitive load reduction and academic stress mitigation.

Table 5: Summary of Key Survey Statistics Among Master’s Students (N = 100)

Note: The results indicated a significant inverse relationship between AI tool usage and perceived stress levels.

Survey results from 100 master’s students indicate that 78% experienced moderate to high levels of cognitive overload, primarily attributed to dense coursework and tight deadlines. A majority (65%) reported using AI tools such as ChatGPT, WolframAlpha, and Grammarly for academic purposes. Regression analysis revealed a significant negative correlation between AI usage and perceived stress (r = -0.42, p < 0.01), suggesting that increased AI interaction is associated with reduced cognitive pressure.

In addition, students who frequently used AI tools demonstrated higher academic confidence and reported better time management. The ANOVA further indicated statistically significant differences in stress reduction based on the frequency of AI tool use (F = 4.56, p < 0.05).

Table 6: AI Usage Frequency and Academic Performance Indicators

 Note: Confidence scores are rated on a 5-point Likert scale. The time management efficiency was self-reported.

Table 5 presents the relationship between the frequency of AI tool usage and two key academic performance indicators among the master’s students: academic confidence and time management efficiency. AI usage was categorized into four levels: Rarely, Sometimes, Often, and Very Often.

Academic Confidence (Mean Scores)

• Trend: Academic confidence improved consistently with increased AI usage. Students who rarely used AI tools reported the lowest confidence level (M = 3.2), whereas those who used them very often reported the highest (M = 4.8).
• Interpretation: This trend suggests that AI tools may serve as cognitive scaffolds to support understanding, clarify complex concepts, and provide reassurance through immediate feedback. According to the Cognitive Load Theory (Sweller, 1988), such tools reduce extraneous cognitive load, allowing learners to focus cognitive resources on meaningful learning (germane load), thereby boosting confidence in academic tasks.
• Relevance to Longevity: Higher academic confidence is often linked to reduced performance anxiety, better mental health, and increased motivation, all of which contribute to lower allostatic load (McEwen & Stellar, 1993). Over time, this can improve psychological resilience and potentially impact the lifespan by reducing stress-related health risks.

Time Management Efficiency (%)

• Trend: There is a strong positive correlation between AI usage and time-management efficiency. Students who rarely used AI reported only 45%time management effectiveness compared to 82% of those who used AI very often.
• Interpretation: AI tools can automate and expedite various academic tasks including summarizing texts, generating outlines, performing calculations, and checking grammar. These efficiencies allow students to manage their academic workloads more effectively. This aligns with the Socio-Technical Systems Theory (Trist & Emery, 1960), which emphasizes the optimization of both social (student effort) and technical (AI tools) systems to enhance performance.
• Relevance to Longevity: Effective time management reduces last-minute stress, promotes work-life balance, and allows for more consistent sleep and nutrition, all of which contribute to lower allostatic load and improved physiological health over time.

Integrated Perspective

• The consistent increase across both indicators suggests a synergistic benefit of AI use: as students feel more confident, they are likely to become more proactive and organized, which further improves their academic performance and mental health.
• These findings support the bio-psycho-social model of longevity, showing how technological aid (AI) can influence psychological well-being and behavioral efficiency, indirectly contributing to lifespan-enhancing environments.

Implications for Policy and Practice

• Curriculum Designers should integrate AI literacy into academic programmes to foster confidence and reduce academic stress.
• University Counseling Units should consider promoting AI tools as part of stress management and productivity interventions.
• Researchers and Educators should explore personalized AI tutors to further support time-constrained learners.

Qualitative Insights

Themes from interview data included “AI as a cognitive assistant,” “digital dependency,” and “emotional reassurance.” Participants reported using AI to break down complex concepts, structure research, and prepare presentations. Some students emphasized how AI helped reduce anxiety, while others raised concerns about overreliance and ethical boundaries.

Theme 1: “AI as a Cognitive Assistant”

Subtheme 1.1: Simplifying Complexity

“Sometimes I feel like the AI translates mathematics into plain English for me. It helps me understand faster than some textbooks do.”

(Participant 6)

Many students have characterized AI tools as intuitive translators with complex content. Tools such as ChatGPT and WolframAlpha have been frequently cited as aids for deciphering abstract theories, suggesting a scaffolding role that aligns with Cognitive Load Theory (Sweller, 1988). By minimizing the extraneous cognitive load, students reported improved comprehension and retention.

Subtheme 1.2: Structuring Thought Processes

“It helps me organize my thoughts, especially when I’m overwhelmed with how to start writing my research paper.”

(Participant 10)

Participants appreciated how AI facilitated not only understanding but also cognitive organization. Whether by generating outlines, guiding problem-solving steps, or suggesting research directions, AI was seen as a partner in structuring academic cognition. This aligns with the germane cognitive load—the mental effort directed toward schema construction and automation.

Theme 2: “Digital Dependency”

Subtheme 2.1: Over-Reliance and Diminished Confidence

“The more I use AI, the more I second-guess myself. I feel like I need to confirm everything with it.”
(Participant 2)

Some participants expressed concerns about cognitive outsourcing. As AI use increased, students reported decreased confidence in their problem-solving abilities. This raises potential concerns regarding automation bias, in which users overly trust algorithmic outputs at the expense of critical thinking.

Subtheme 2.2: Ethical and Pedagogical Boundaries

“Sometimes I wonder, where do I draw the line? Is it still my work if I use AI for everything?”
(Participant 13)

The line between assistance and academic dishonesty emerged as a gray area. While students recognized the utility of AI, they voiced uncertainty about its ethical use, raising questions about originality, authorship, and long-term skill development. This reflects the broader ethical tension within Socio-Technical Systems Theory (Trist & Emery, 1960), where human and technological systems must remain in productive balance.

Theme 3: “Emotional Reassurance”

Subtheme 3.1: Reducing Academic Anxiety

“There’s something calming about having an answer—even if it’s wrong, at least I’m not stuck anymore.”
(Participant 8)

Several students have described AI as a buffer against academic paralysis and anxiety. When stuck or overwhelmed, AI offered not only cognitive direction but also emotional calmness. The interaction was likened to having a “study companion, ” which provides instant, judgment-free feedback.

Subtheme 3.2: Companionship in Isolation

“During late nights, it’s just me and the AI. It keeps me company in a weird but helpful way.”
( Participant 4)

Figure1: Thematic Map: Qualitative Insights on AI Use in Academia

Source: Qualitative interview data collected from 15 Master’s students in mathematics at the Catholic University of Ghana (2025).

This thematic map was derived from primary data collected through semi-structured interviews and analyzed using Braun and Clarke’s (2006) thematic analysis approach. It visually summarizes the emergent themes and subthemes that describe students’ perceptions and experiences of using AI tools in academic work.

AI has emerged as a pseudo-social support system in the context of remote learning and academic solitude. This finding connects to Allostatic Load Theory (McEwen, 1998), which posits that emotional regulation—whether from human or artificial agents—can reduce chronic stress and support long-term physiological health

Table 7: Cross-Thematic Reflections

The convergence of these themes supports a more nuanced perspective: AI is not merely a tool but also a cognitive-social actor in the academic ecosystem. It influences mental effort, ethical reasoning, and emotional balance, which are essential for cognitive longevity.

Theoretical Integration

• Cognitive Load Theory: AI supports schema acquisition by reducing extraneous and enhancing germane cognitive load.
• Allostatic Load Theory: Emotional support from AI interactions can modulate physiological stress responses.
• Socio-Technical Systems Theory: Ethical tensions highlight the importance of a balanced co-evolution between humans and intelligent systems.

Theoretical Synthesis

• Cognitive Load Theory: AI tools assist in reducing extraneous cognitive load by simplifying complex tasks and offering instant feedback. This allows students to allocate more resources to germane loads, thereby enhancing their deep learning.
• Allostatic Load Theory: The decrease in academic stress aligns with the concept of reduced physiological burden. Regular AI use contributes to perceived emotional control, which is a critical factor in preventing chronic stress responses and in supporting long-term health.
• Sociotechnical Systems Theory: The academic environment is evolving into a human-AI collaborative space. AI serves not merely as a tool but also as an integral component of the learning ecosystem, influencing not only outcomes but also social and institutional processes.

Implications for Longevity: By alleviating mental strain and improving emotional resilience, AI indirectly contributes to long-term health and well-being, both key markers in the discourse on human longevity. This insight positions AI not only as a productivity enhancer, but also as a potential public health intervention in high-stress educational settings.

Policy Implications & Feasibility Matrix

Interpretation of Key Findings

This study demonstrates that artificial intelligence (AI) tools, when integrated thoughtfully, can significantly alleviate cognitive overload among postgraduate mathematics students. These findings have three major implications for educational policy and institutional governance.

Institutional Integration of AI Tools

Finding-based Rationale

65% of surveyed students reported using AI tools for academic work, and regression analysis showed a statistically significant negative correlation (r = -0.42, p < 0.01) between AI use and perceived stress.

Policy Implication:

Institutions such as the Catholic University of Ghana should integrate vetted AI platforms into formal academic support structures such as university libraries, writing centers, and course-specific learning portals.

Example:

• Licensing partnerships with ChatGPT or Grammarly EDU for student access
• Embedding AI chatbots in learning management systems (e.g., Padlet TA)

Digital Literacy and Ethical AI Usage Training

Finding-based Rationale

Qualitative data revealed concerns regarding digital dependency, ethical ambiguity, and diminished critical thinking due to unregulated AI reliance.

Policy Implication:

There is a need to institutionalize AI literacy modules within graduate curricula that emphasize the ethical, responsible, and balanced use of AI technologies.

Example:

• Orientation sessions or micro-courses on “AI Ethics and Academic Integrity”
• Case studies and simulation exercises on AI misuse in coursework

Mental Health and Cognitive Well-being Strategies

Finding-based Rationale

78% of students experienced moderate to high cognitive overload, and interviewees cited AI as providing “emotional reassurance” and buffering academic anxiety.

Policy Implication:

Universities should recognize AI tools as potential complements to psychological support systems and consider incorporating AI-assisted cognitive wellness initiatives into broader student support services.

Example:

• AI-powered journaling or reflection apps integrated into counseling services
• Workshops on “Digital Companionship and Academic Wellness”

Feasibility Matrix

A structured matrix is provided to assess the feasibility, impact, and implementation scope of each policy action based on the cost, stakeholder buy-in, and required infrastructure.

Table 8: Feasibility Matrix

Public Health and National Relevance

The implications of this study extend beyond the academic environment to the broader domains of public health, youth development, and national education policy. If left unmanaged, cognitive overload can escalate into chronic stress, burnout, and long-term psychological distress. This is particularly pertinent for postgraduate students engaged in rigorous fields, such as mathematics, where the demands of abstraction, problem-solving, and continuous assessment are inherently taxing.

AI and Youth Mental Health

Findings from this study revealed that 78% of master’s students reported moderate to high levels of cognitive stress, yet those who regularly engaged with AI tools (e.g., ChatGPT, Grammarly, Wolframe Alpha) experienced reduced stress and greater academic self-efficacy. These outcomes suggest that AI-assisted learning environments may serve as digital buffers against stress-related academic attrition.

This opens a critical policy window for integrating AI-based mental wellness support into national youth and tertiary-education strategies. The Ministry of Health and Education has explored the following:

• AI-powered academic counseling systems embedded within national student portals
• Publicly funded AI-enhanced educational apps for secondary and tertiary students
• Partnerships with universities to pilot AI-based stress management programs

National Human Capital and Cognitive Longevity

From a macroeconomic and developmental perspective, cognitive longevity—sustained mental clarity, memory retention, and resilience—constitutes a vital component of national human capital. When students become cognitively overwhelmed, their productivity, creativity, and academic persistence are compromised.

Conversely, cognitively supported learning systems foster graduates who are better prepared for complex problem-solving and innovation skills essential to Ghana’s long-term knowledge economy. In this context, AI tools are not merely educational aids, but are capable of enhancing national resilience and workforce preparedness.

AI for Public Health Innovation

Given that cognitive overload and stress are upstream determinants of mental health disorders, AI’s potential of AI for early detection and intervention should be explored as a novel digital health strategy. Integration with Ghana Health Service’s mental health programs could allow:

• AI-assisted screening for academic stress through university clinics
• AI chatbots to support anonymous peer counseling and triage
• Use of AI to predict dropout risks or mental health crises based on behavioral and academic patterns

Cross-Sector Collaboration

This study underscores the need for collaborative policymaking across the domains of:

• Higher Education (AI integration and curriculum design),
• Public Health (mental wellness programs), and
• Digital Transformation (technology governance and infrastructure).

Embedding AI into the broader national strategy for cognitive development, student well-being, and digital literacy, Ghana can position itself as a leader in tech-enhanced human development across Africa.

Together, these findings and insights highlight the transformative potential of artificial intelligence in shaping cognitively supportive, emotionally resilient, and health-enhancing academic environments. AI’s role should be envisioned not as a technological luxury but as a strategic enabler of public health equity and educational innovation.

Strategic Considerations

The integration of AI into academic life, particularly within the context of postgraduate mathematics education, demands a multifaceted strategic approach. Based on the findings, the following critical considerations should inform policy and institutional decisions:

Cost-Benefit Alignment

Strategic Insight:

Although AI platforms and digital training programs involve upfront costs—ranging from software licensing to infrastructure upgrades—the long-term benefits far outweigh the investment. These benefits include enhanced academic performance, reduced dropout rates due to cognitive overload, improved time management, and increased student satisfaction.

Recommendation: Institutions should consider cost-sharing models, external grants, or open-source AI options (e.g., Open AI’s API for education, Google’s Teachable Machine) to mitigate financial barriers.

Stakeholder Engagement and Participatory Policy Design

Strategic Insight:

AI-related interventions are most effective when stakeholders—especially students and faculty—are actively involved in their design and rollout. Engaging end-users enhances acceptance, contextual relevance, and ethical accountability.

Recommendation:

Create multidisciplinary working groups that include students, instructors, technologists, and ethicists to co-develop AI literacy programs, academic integrity policies, and usage guidelines tailored to the university’s cultural and academic context.

Ethical Guardrails and Governance Structures

Strategic Insight:

The qualitative data revealed deep concerns about over-reliance on AI, potential academic dishonesty, and erosion of critical thinking. Without ethical boundaries, the risk of undermining educational goals increases.

RECOMMENDATION:

Institutions must craft clear AI governance frameworks that define responsible use, including issues of attribution, transparency, authorship, and plagiarism. This can be supported by:

• Academic honor codes updated for AI contexts
• Ethical AI use clauses in syllabi
• Faculty training on AI-assisted assessments

Interoperability with Existing Educational Systems

Strategic Insight:

The socio-technical systems framework underscores the need for seamless integration of AI tools within current pedagogical and administrative structures. AI should not function as an external add-on but as a core component of digital academic ecosystems.

Recommendation:

Leverage AI plug-ins and APIs that integrate with existing platforms such as:

• Moodle (for personalized learning paths)
• Turnitin (for originality checking with AI detection modules)
• Library portals (for AI-assisted citation and research navigation)

Sustainability and Scalability

Strategic Insight:

Pilot programs, while effective in demonstrating proof of concept, often fail to scale due to inadequate planning, lack of funding continuity, or leadership turnover.

Recommendation: Design AI integration as part of a long-term digital transformation strategy, backed by:

• Institutional KPIs
• Faculty and student feedback loops
• Periodic impact assessments on cognitive wellness and learning outcomes

Inclusive and Context-Sensitive Implementation

Strategic Insight:

Digital equity must be at the heart of any AI-based educational strategy. Not all students have equal access to the tools or the bandwidth—technological or cognitive—to engage effectively with AI.

Recommendation:
Design inclusive rollouts that consider:

• Offline AI tools or local servers for low-bandwidth settings
• Training materials in multiple languages or levels of technical literacy
• Gender- and disability-sensitive approaches to AI integration

Summary Table 9: Strategic Considerations and Responses

 RECOMMENDATIONS

Based on the study’s findings, theoretical integration, and practical implications, the following recommendations are proposed at short-, medium-, and long-term levels to guide institutions, policymakers, and researchers in leveraging AI to support cognitive well-being and academic longevity:

• Short-Term Recommendations (0–6 Months)

• Develop and Integrate AI Literacy Programs:

Implement training modules or workshops on how to effectively and ethically use AI tools in academic research, particularly for STEM-focused graduate programs.
Responsible units: Academic Affairs, ICT Unit

• Formalize Access to Approved AI Tools:

Provide vetted access to AI platforms like ChatGPT, Grammarly EDU, and WolframAlpha through institutional logins or learning platforms. Responsible units: Library Services, Department Heads

• Raise Awareness on Cognitive Load and AI Support:

Host public lectures or awareness campaigns to sensitize students and faculty about cognitive overload and the role AI can play in managing academic pressure.

10.2 Medium-Term Recommendations (6–18 Months)

• Embed AI-Ethics in Curricula:

Design AI-focused ethics courses tailored to graduate students, addressing concerns about overreliance, authorship, and originality. Collaborating departments: Philosophy, Computer Science, Educational Psychology

• Create a University Policy on AI Use in Academia:

Draft clear guidelines that define acceptable use of AI tools in coursework, thesis writing, and examinations to ensure consistency and fairness.

• Train Faculty in Pedagogical Integration of AI:

Equip lecturers with skills to incorporate AI into instructional methods without compromising critical thinking development.

10.3 Long-Term Recommendations (2+ Years)

• Establish AI-Supported Cognitive Well-being Units:

Institutionalize AI-augmented wellness services that offer academic time management tools, AI journaling for stress relief, and virtual mentorship via AI platforms.

• Conduct Longitudinal Studies on AI and Cognitive Load:

Encourage faculty and postgraduate research into the evolving impact of AI on learning, attention, and student performance over time.

• Develop Interdisciplinary AI and Education Research Hubs:

Position the Catholic University of Ghana as a regional leader in AI-in-Education research by fostering partnerships across disciplines.

CONCLUSION

The study sought to understand how artificial intelligence would improve longevity of life in the context of the observed cognitive overload, specifically focusing on master’s students in mathematics at the Catholic University of Ghana. A mixed-methods approach shed light on the dual aspect of AI tools serving as cognitive aids reducing mental overload and facilitating learning and as emotional buffers against academic stress.

Findings established that a considerable number of students have experiences of cognitive overload at moderate to high levels and were heavily relying on AI tools to cope with the heavier academic workload. The quantitative measure indicated that AI use has a statistically significant inverse relationship with stress, and qualitative interviews captured the more nuanced experiences ranging from empowerment to moral dilemmas.

A framework integrating Cognitive Load Theory, Allostatic Load Theory, and Socio-Technical Systems Theory was developed by the study to explain how technology can underpin academic performance, as well as sustain cognitive and emotional health over the long run.

As education systems forge a path in the post-pandemic era of digitalization, this research points out that human-centered AI design amid ethical considerations and pedagogical innovations holds an opportunity for transformation toward improved academic achievement and mental wellbeing. Future work should explore intervention approaches across disciplines, diverse age groups, and evolving definitions of cognitive longevity in increasingly digitalized learning ecosystems.

REFERENCES

1. Amponsah, M., & Owolabi, H. O. (2011). Perceived stress levels of university students in Ghana: A case study. International Journal of Research in Social Sciences, 2(3), 1-10.
2. Baxter, G., & Sommerville, I. (2011). Socio-technical systems: From design methods to systems engineering. Interacting with Computers, 23(1), 4-17.
3. Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.
4. Creswell, J. W., & Plano Clark, V. L. (2017). Designing and conducting mixed methods research (3rd ed.). Sage Publications.
5. Fries, J. F. (2002). Aging, natural death, and the compression of morbidity. Bulletin of the World Health Organization, 80(3), 245–250.
6. Kirschner, P. A. (2002). Cognitive load theory: Implications of cognitive load theory on the design of learning. Learning and Instruction, 12(1), 1-10.
7. Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson Education.
8. Lupien, S. J., McEwen, B. S., Gunnar, M. R., & Heim, C. (2009). Effects of stress throughout the lifespan on the brain, behaviour and cognition. Nature Reviews Neuroscience, 10(6), 434-445.
9. McEwen, B. S., & Stellar, E. (1993). Stress and the individual: Mechanisms leading to disease. Archives of Internal Medicine, 153(18), 2093–2101.
10. Olshansky, S. J., Passaro, D. J., Hershow, R. C., et al. (2018). A potential decline in life expectancy in the United States in the 21st century. New England Journal of Medicine, 352(11), 1138–1145.
11. Paas, F., Renkl, A., & Sweller, J. (2003). Cognitive load theory and instructional design: Recent developments. Educational Psychologist, 38(1), 1–4.
12. Roll, I., & Wylie, R. (2016). Evolution and revolution in artificial intelligence in education. International Journal of Artificial Intelligence in Education, 26, 582–599.
13. Russell, S., & Norvig, P. (2016). Artificial intelligence: A modern approach (3rd ed.). Pearson.
14. Sapolsky, R. M. (2004). Why zebras don’t get ulcers. Henry Holt and Company.
15. Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Polity Press.
16. Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285.
17. Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory. Springer.
18. Trist, E. L., & Bamforth, K. W. (1951). Some social and psychological consequences of the longwall method of coal-getting. Human Relations, 4(1), 3–38.

APPENDICES

Appendix A: Survey Instrument

Title: AI Use and Cognitive Load Survey for Postgraduate Mathematics Students

Purpose: This survey seeks to assess the perceived cognitive load among postgraduate mathematics students and their usage of Artificial Intelligence (AI) tools in managing academic demands.

Section 1: Demographic Information

• Age: ___
• Gender: ☐ Male ☐ Female ☐ Other
• Program of Study: ____________________
• Year of Study: ☐ Year 1 ☐ Year 2
• Access to Internet at home: ☐ Yes ☐ No
• Frequency of AI tool usage: ☐ Daily ☐ Weekly ☐ Monthly ☐ Rarely ☐ Never

Section 2: AI Usage Patterns

• Which of the following AI tools have you used in your academic work? (Check all that apply)

☐ ChatGPT ☐ Grammarly ☐ WolframAlpha  ☐ Mathway  ☐ Other (specify): _____________

• For what purposes do you use AI tools? (Check all that apply)

☐ Problem solving (math/statistics) ☐ Writing and grammar correction ☐ Research and referencing ☐ Study planning and organization ☐ Emotional support/stress relief ☐ Other: _________________________

• How helpful are AI tools in reducing academic stress for you?

☐ Very helpful ☐ Somewhat helpful ☐ Neutral ☐ Not helpful

• On average, how many hours a week do you interact with AI tools for academic purposes?

☐ <1 hour ☐ 1–3 hours ☐ 4–6 hours ☐ 7–10 hours ☐ >10 hours

Section 3: Cognitive Load Perception Scale

On a scale from 1 (strongly disagree) to 5 (strongly agree), indicate how much you agree with the following statements:

 Section 4: Academic Confidence & Stress

• How often do you feel confident in managing your academic responsibilities? ☐ Never ☐ Rarely ☐ Sometimes ☐ Often ☐ Always
• How frequently do you feel anxious or overwhelmed with coursework? ☐ Never ☐ Rarely ☐ Sometimes ☐ Often ☐ Always
• Do you feel AI use helps in maintaining a healthier study-life balance? ☐ Yes ☐ No ☐ Not sure

Appendix B: Interview Guide

Title: Semi-Structured Interview Guide – Exploring AI Use and Cognitive Support

Purpose: To gather in-depth insights from master’s students in mathematics about their experiences with cognitive overload and the use of AI tools in academic settings.

Instructions to the Interviewer:

Build rapport with participants before recording.

Use open-ended questions to encourage deeper reflection.

Allow participants to elaborate with examples.

Core Interview Questions

Background and Learning Experience

Can you describe your typical academic workload as a master’s student in mathematics?

What aspects of your coursework or research do you find most cognitively challenging?

Use of AI in Academics

What AI tools do you use most frequently in your academic life?

How do these tools assist you in learning or managing your tasks?

Can you give a specific example where an AI tool helped reduce stress or confusion?

Emotional and Cognitive Impact

Do you feel that AI tools help in reducing your academic stress or anxiety?

In what ways do AI tools make you feel more (or less) confident in your work?

Have you ever felt dependent on AI for completing your assignments or learning?

Ethical Considerations and Learning Integrity

What do you think about the ethical use of AI tools in academic work?

Have you or your peers discussed concerns about overreliance or misuse of AI?

How should universities guide students on responsible AI use?

Future Expectations

What improvements would you like to see in the integration of AI in education?

In your opinion, can AI be a long-term solution to reducing cognitive stress in postgraduate education?