# Strategic Integration of Generative AI in Higher Education: Analysis of Usage, Competencies, and Policy Measures for the Science Education of Bangladesh

Mehedi Hasan Anik, S M Hafizur Rahman

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Mehedi Hasan Anik, S M Hafizur Rahman. Strategic Integration of Generative AI in Higher Education: Analysis of Usage, Competencies, and Policy Measures for the Science Education of Bangladesh. 2025. [hal-04904869]

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# Strategic Integration of Generative AI in Higher Education: Analysis of Usage, Competencies, and Policy Measures for the Science Education of Bangladesh

¹Mehedi Hasan Anik,

¹S M Hafizur Rahman

¹Institute of Education and Research, University of Dhaka, Bangladesh

**Highlights:**

1. Generative AI in higher education is increasingly shaping the academic landscape, particularly in the context of the Fifth Industrial Revolution (5IR), which emphasizes human-AI collaboration to enhance learning, research, and assessment.

2. Despite the growing use of Generative AI tools such as ChatGPT, Google Bard, Grammarly, and QuillBot, there is no formal policy or institutional framework guiding AI integration in Bangladeshi universities, raising concerns over academic integrity, competency development, and ethical use.

3. While AI adoption is increasing in the academy, there is limited research on AI in Bangladesh and no research identifying the usage levels, competency acquisition, and its pedagogical impact, creating a gap in understanding how AI literacy is evolving in Bangladesh's higher education sector.

4. This study aims to analyze AI usage levels, evaluate competencies gained by SMTE students and IER faculty members, and propose structured policy measures for AI integration in teaching, learning, and assessment.

5. The research follows a three-phase sequential explanatory mixed-method approach, beginning with quantitative surveys to assess AI adoption and competency levels, followed by qualitative key-informant interviews (KII) and a policy workshop to develop strategic planning recommendations.

6. Third-generation cultural-historical Activity Theory (CHAT) serves as the theoretical framework, examining contradictions, collaboration, and shared motives in AI adoption across multiple academic stakeholders, facilitating a comprehensive analysis of AI integration challenges and opportunities.

7. A structured conceptual model will be developed to outline interactions between students, faculty, AI tools, policies, and institutional regulations, forming a systematic roadmap for structured AI adoption in higher education.

8. The study investigates how Generative AI is transforming pedagogy, including personalized learning, automated grading, and curriculum development, while also addressing concerns such as over-reliance on AI-generated content and the digital divide.

9. The study will provide empirical grounds for strategic planning by offering evidence-based insights into AI usage patterns, competency, and gaps, ensuring informed decision-making for AI integration in higher education.

10. Findings will offer data-driven insights into AI adoption, competency gaps, and institutional barriers, presenting strategic recommendations for AI literacy programs and national-level policies to ensure ethical, effective, and sustainable AI integration in Bangladeshi higher education.

**Abstract**

The Fifth Industrial Revolution (5IR) has accelerated the adoption of Generative AI (GenAI) in higher education, significantly impacting teaching, learning, and research. While tools like ChatGPT, Google Bard, Grammarly, and QuillBot have enhanced academic efficiency, the unstructured integration of AI in Bangladeshi higher education institutions raises concerns regarding academic integrity, competency development, and policy frameworks. Despite increasing AI adoption among students and educators, there is no systematic assessment of AI usage levels, competencies gained, or strategic measures for AI integration in Bangladesh's higher education sector. This study aims to analyze the extent of GenAI use, evaluate AI competencies among Science, Mathematics, and Technology Education (SMTE) students and Institute of Education and Research (IER) faculty members, and propose policy-driven AI integration strategies. Employing a multiphase mixed-method research design, this study follows an explanatory sequential approach, starting with a quantitative survey to assess AI usage and competency levels, followed by qualitative key-informant interviews (KII) and a policy workshop to develop strategic planning recommendations. Findings will provide empirical insights into AI adoption, competency gaps, and necessary policy measures, contributing to a structured, ethical, and effective AI integration framework for Bangladeshi higher education.

**Keywords:** Generative AI, Higher Education, Fifth Industrial Revolution, AI Competencies, Mixed-Method Research

**Introduction**

The rapid evolution of Generative Artificial Intelligence (GenAI) is a defining feature of the emerging Fifth Industrial Revolution (5IR), which emphasizes human-AI collaboration to enhance efficiency, creativity, and personalized learning in education (Urmeneta et al, 2024). Unlike previous industrial revolutions that focused solely on automation, 5IR integrates AI with human-centered approaches, making AI-driven tools essential in higher education for research, content generation, assessment, and personalized learning experiences (Rane et al., 2023). Widely used AI tools in academia, such as ChatGPT, Google Bard, Grammarly, and QuillBot, assist students and researchers in academic writing, literature reviews, data analysis, and coding support, significantly influencing pedagogical practices (Anik et al., 2024; Shirin, 2022). In Bangladesh, university students increasingly rely on GenAI for assignments, thesis preparation, and knowledge synthesis, while faculty members explore its
applications in curriculum development, automated grading, and AI-assisted teaching methodologies (Hassan, 2023). However, despite its growing adoption, AI integration in Bangladeshi higher education remains unstructured, with no formal policies or institutional frameworks guiding its ethical and strategic use (Tamanna & Sinha, 2024). While AI offers substantial benefits, concerns persist regarding academic integrity, over-reliance on AI-generated content, digital literacy gaps, and unequal access to AI-powered learning tools (Islam, 2022; Rudro et al., 2024). Given the transformative role of AI in education and the demands of the 5IR, it is crucial to develop a structured framework for AI adoption. This study examines the strategic integration of Generative AI in higher education, particularly within the Science, Mathematics, and Technology Education (SMTE) department and faculty members of the Institute of Education and Research (IER), University of Dhaka, identifying existing AI competencies, challenges, and policy recommendations for responsible AI use in teaching, learning, and research.

## Problem Statement

The integration of Generative AI (GenAI) in higher education is reshaping teaching, learning, and research globally, with tools like ChatGPT, Gemini, Grammarly, and QuillBot enhancing academic writing, personalized learning, and data-driven decision-making (UNESCO, 2023). However, in Bangladesh, AI adoption remains informal and unstructured, particularly in the Science, Mathematics, and Technology Education (SMTE) department at the Institute of Education and Research (IER), University of Dhaka. While students use AI for academic writing, thesis preparation, and research assistance, this occurs without institutional policies or guidelines, and faculty members struggle with AI integration due to limited training and institutional support (Anik et al., 2024; Shirin, 2022). The absence of structured AI policies and competency development programs raises concerns about academic integrity, plagiarism, over-reliance on AI-generated content, and declining analytical skills (Rudro et al., 2024). Additionally, faculty members lack AI literacy, further limiting AI's educational benefits (Hassan, 2023). Infrastructure and policy barriers also restrict AI adoption in public universities, which face financial constraints and lack digital infrastructure, unlike private institutions that are gradually integrating AI-powered learning systems (Islam, 2022; Tamanna & Sinha, 2024). Despite recognition from the University Grants Commission (UGC), no national policy or roadmap exists for AI integration, leaving students and educators to navigate its adoption independently, resulting in inconsistent and fragmented use (Bina, 2023).

## Research Gap

While international research has extensively explored the role of AI in higher education, there is limited empirical evidence on the structured integration of Generative AI in Bangladeshi universities. Existing studies have primarily examined the potential benefits and ethical concerns of AI in education but have not assessed the actual usage patterns and competencies developed by students and faculty members (Anik et al., 2024; Sultana & Faruk, 2024). Furthermore, while some research highlights faculty perceptions of AI in education, no study has systematically evaluated how AI literacy and competency gaps impact teachers' pedagogical practices in Bangladesh (Shirin, 2022). Moreover, although students in the SMTE department at IER have informally adopted AI tools for academic purposes, there is no comprehensive study analyzing their competency development and the extent of their reliance on AI. Similarly, while faculty members across all IER departments are expected to integrate digital tools into their teaching practices, little is known about their preparedness for AI-assisted education. The lack of structured AI training programs for educators further widens this gap, leaving many instructors unprepared to incorporate AI tools into curriculum design, student assessments, and interactive learning activities (Wafik, 2024).

Another key gap in the literature is the absence of strategic policy recommendations tailored to the Bangladeshi context. While some higher education institutions globally have adopted AI literacy programs and AI-powered curricula, Bangladesh has yet to establish national or institutional guidelines to ensure AI integration aligns with best practices and ethical standards (Tamanna & Sinha, 2024). Additionally, no research has proposed a structured framework that defines AI competencies for both students and faculty members within Bangladeshi universities. Given these gaps, this study aims to bridge the knowledge deficit by systematically analyzing AI usage levels, competency development, and strategic policy needs within the SMTE student body and all faculty members of IER.

## Purpose of the Study

This study aims to examine the integration of Generative AI in higher education in Bangladesh, focusing on SMTE students and all faculty members of IER at the University of Dhaka. It seeks to assess AI usage levels, identify AI competencies informally gained through engagement with AI tools, and evaluate the challenges of AI adoption in higher education. Using Cultural-Historical Activity Theory (CHAT) as an analytical framework, the study will analyze how AI tools mediate learning and teaching, identify contradictions in AI adoption, and propose strategies to enhance AI literacy among students and faculty. The research aims to inform policy recommendations for responsible AI integration by addressing competency gaps, infrastructure needs, and faculty preparedness for AI-assisted teaching. It will contribute to the development of structured AI literacy programs aligned with UNESCO's AI Competency Framework for Education and provide practical insights for educators, policymakers, and institutional leaders in establishing guidelines for AI-enhanced learning, assessment, and teacher training. Additionally, the study will offer data-driven recommendations to support the UGC and university authorities in formulating national policies for sustainable AI integration in tertiary education in Bangladesh.

## Theoretical Framework

Third-generation Cultural-Historical Activity Theory (CHAT), developed by Engeström (2001), extends earlier models by analyzing interactions between multiple activity systems, emphasizing shared objects—collaborative goals shaped by the motives of different stakeholders. Unlike its predecessors (last two generations), this version of CHAT focuses on contradictions, or tensions between rules, tools, and roles within and across systems, which drive expansive learning and transformation. Each activity system comprises a subject (students, teachers), object (the motive driving their actions), mediating artifacts (Generative AI tools like ChatGPT), community, rules, and division of labor, forming a dynamic structure. This framework is particularly relevant in education, where AI integration reshapes teaching, learning, and institutional policies by influencing competency development and pedagogical strategies. As contradictions are resolved, new practices emerge, fostering more adaptive, collaborative, and innovative educational environments.
<figure><img src="image_2.png" /><figcaption>Figure 01: Theoretical Framework of the Proposed Study</figcaption></figure>

## Conceptual Framework

The conceptual framework of this study is structured using Third-Generation Cultural-Historical Activity Theory (CHAT) to analyze the integration of Generative AI in teaching, learning, and assessment within the Institute of Education and Research (IER) at the University of Dhaka. The framework illustrates the interaction between two primary activity systems: SMTE students (Subject-01) and IER teachers (Subject-02), both using Generative AI as a mediating artifact. Each group is driven by overall motives (Object-01) and academic motives (Object-02), which converge into a shared motive (Object-03) for integrating AI in education. The usage level of Generative AI and the AI competencies gained by students and teachers are adapted from Romero et al. (2023) and UNESCO (2024a, 2024b). The rules governing these activity systems are shaped by policy and strategic planning, while the communities involved (IER teachers, SMTE students, and IER authorities) interact through a division of labor to facilitate AI adoption. This framework also aligns with the UN Strategic Planning Framework (United Nations, n.d.), emphasizing the role of structured AI policies in education.

## Methodology

*Research Design.* This study employs a multiphase mixed-method research design following an explanatory sequential mixed-method approach (Creswell & Plano Clark, 2017) to examine the integration of Generative AI in higher education. The research unfolds in three phases, beginning with a quantitative survey to assess AI usage levels and competencies gained among SMTE students and IER faculty members, statistically identifying differences between the two groups (Cohen et al., 20). The findings from this phase will inform Phase 2, a qualitative study, where Key Informant Interviews (KII) will be conducted with AI experts in education disciplines to explore the pedagogical impact and challenges of AI integration (Denzin & Lincoln, 2018). The results from both phases will then shape Phase 3, a qualitative validation process, where a workshop using the UN Strategic Planning Framework (United Nations, n.d.) will be conducted to propose strategic planning, performance measures, and initiatives for AI adoption in teaching, learning, and assessment. The mixed-method approach ensures data triangulation, enhancing the validity and reliability of findings while supporting evidence-based policy recommendations for AI integration in higher education (Bryman, 2016).

<figure><img src="image_3.png" /><figcaption>Figure 02: Research Design and the Phases of the Study</figcaption></figure>

*Sample Size and Sampling.* This study determines the sample size for learners from the Science, Mathematics, and Technology Education (SMTE) department and faculties from the Institute of Education and Research (IER) using Cochran's formula. The total population of learners in SMTE is 160, from which 114 were selected as the sample (44 males and 70 females). Similarly, the total faculty population in IER is 50, and 45 faculties were chosen as the sample (28 males and 17 females). The study employs simple random sampling (SRS), ensuring that each participant has an equal probability of selection, which enhances representativeness and reduces selection bias (Cochran, 1977). SRS is particularly suitable for studies requiring statistical generalizability (Krejcie & Morgan, 1970). A stratified random sampling approach was used for faculty selection to maintain a proportional representation of male and female members, ensuring diversity in perspectives (Creswell & Creswell, 2017). The chosen sample sizes were determined with a 95% confidence level and a 5% margin of error, making the findings statistically reliable while ensuring feasibility in data collection (Israel, 1992). This methodological approach aligns with best practices in educational research sampling, enhancing the validity and reliability of the study outcomes.

*Data Collection Tools.* This study employs a structured questionnaire survey as the primary data collection tool for the quantitative phase, first identifying whether SMTE students and IER faculty members use Generative AI tools or not. For those who use AI, the survey will further assess
their usage levels and AI competencies gained, capturing data on frequency, purpose, and perceived competency development (Cohen et al., 2002). Structured surveys ensure standardized responses, facilitating statistical comparisons and robust data analysis (Creswell & Creswell, 2017). The qualitative phase will include Key Informant Interviews (KII) with AI experts in education disciplines, using a semi-structured format to explore the impact, challenges, and pedagogical implications of AI integration (Denzin & Lincoln, 2018). Additionally, a workshop involving policy experts, educators, and students will be conducted, where participants will provide written and verbal reflections on strategic planning, performance measures, and policy initiatives for AI adoption in higher education. The quantitative data analysis will begin with descriptive statistics to determine the proportion of AI users vs. non-users, followed by analyzing usage levels and AI competency development among users. Inferential statistics (t-tests) will identify significant differences in AI adoption and competencies between students and faculty members (Creswell & Plano Clark, 2017). The qualitative data from interviews and workshops will be analyzed using thematic analysis, identifying recurring themes related to barriers, opportunities, and policy recommendations for AI integration (Braun & Clarke, 2006). By employing a sequential mixed-method approach, this study ensures a comprehensive and data-driven understanding of AI adoption in higher education, forming the basis for well-informed policy recommendations and competency development strategies (Bryman, 2016).

*Data Analysis Planning.* This study employs a structured questionnaire survey to determine whether SMTE students and IER faculty members use Generative AI, followed by assessing usage levels and competencies among AI users. Structured surveys ensure standardized responses and allow for statistical analysis (Cohen et al., 2018). The qualitative phase involves Key Informant Interviews (KII) with AI experts to explore the impact, challenges, and pedagogical implications of AI adoption (Denzin & Lincoln, 2018), alongside a workshop where policy experts, educators, and students will provide written and verbal reflections on strategic planning for AI integration. Descriptive statistics will analyze AI user distribution, usage frequency, and competency levels, while inferential statistics (t-tests) will identify differences in AI adoption between students and faculty (Creswell & Creswell, 2017). Thematic analysis will be used to interpret qualitative data, identifying key themes related to barriers, opportunities, and strategic recommendations (Braun & Clarke, 2006). By employing a sequential mixed-method approach, this study ensures a comprehensive, data-driven understanding of AI adoption in higher education (Bryman, 2016).

## Conclusion

The integration of Generative AI (GenAI) in higher education is an inevitable shift driven by the demands of the Fifth Industrial Revolution (5IR), emphasizing human-AI collaboration for enhanced learning and research. While AI-powered tools such as ChatGPT, Google Bard, Grammarly, and QuillBot offer significant benefits in academic writing, assessment, and personalized learning, their unregulated use in Bangladeshi universities raises concerns about academic integrity, competency gaps, and policy deficiencies. This study aims to address these challenges by assessing AI adoption, identifying competencies gained, and proposing strategic policy measures for AI integration within the Science, Mathematics, and Technology Education (SMTE) department and faculty members of the Institute of Education and Research (IER), University of Dhaka. Through an explanatory sequential mixed-method design followed by a workshop (qualitative phase), this research will provide empirical insights into AI usage levels, competency development, and institutional readiness, informing data-driven recommendations for structured AI adoption in Bangladeshi higher education. By aligning findings with the Cultural-Historical Activity Theory (CHAT) framework and the UN Strategic Planning Framework, this study contributes to the ethical, effective, and sustainable integration of AI in education, ensuring that both students and faculty members are equipped with the necessary competencies to navigate an AI-driven academic landscape.
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Authors:

¹Mehedi Hasan Anik,

²S M Hafizur Rahman

¹MEd, Department of Science, Mathematics, and Technology Education (SMTE), Institute of Education and Research, University of Dhaka, Bangladesh

² Professor, Institute of Education and Research, University of Dhaka, Bangladesh.