Submitted By
Safeena Arshad
safeenaarshad.ms@gmail.com
+923045709479
School of Computing Engineering and Technology
Robert Gorden University Aberdeen
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Table of Contents
Submitted By ............................................................................................................................................... 1
1. Explanation of Why the I Wishes to Undertake Studies at Robert Gordon University ........... 3
2. Proposed Study Title/Area of Research Interest .......................................................................... 3
3. Rationale and Background ............................................................................................................. 3
Major Approach.................................................................................................................................. 4
Significance of the Research............................................................................................................... 5
4. Study Aims and Objectives ............................................................................................................ 5
5. Literature Review ........................................................................................................................... 6
Personalized Learning ........................................................................................................................ 7
Interactive Learning ........................................................................................................................... 7
Ethical Considerations........................................................................................................................ 7
Future Directions for Research ......................................................................................................... 7
6. Proposed Research Methods .......................................................................................................... 7
Data Analysis Techniques Overview: ................................................................................................ 8
7. Benefits, Contributions, and Applications of Findings ................................................................ 9
8. References/Bibliography ................................................................................................................ 9
9. Career Aspirations/Motivation for the Desire to Gain a Higher Degree Qualification .......... 10
10. Source of Funding to Support Study ......................................................................................... 10
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Leveraging Generative AI to Enhance Student Learning Outcomes: A Framework for
Educational Innovation
1. Explanation of Why the I Wishes to Undertake Studies at Robert Gordon
University
Robert Gordon University (RGU) has a robust research culture in the area of emerging
technologies, specifically artificial intelligence (AI) and its applications in education. RGU's cross-
disciplinary research culture is well-suited for my academic pursuits, specifically analyzing the
potential of generative AI to drive student learning outcomes. RGU's focus on innovation in higher
education provides a perfect setting to carry out this research. Additionally, I have gone through
the profiles of prospective supervisors Dr. Mark Zarb, Dr. Elliot Pirie, and Rachel McGregor
whose research areas are closely related to my research interests. Their academic qualifications
and current research on AI and education further strengthen my application to RGU.
2. Proposed Study Title/Area of Research Interest
Leveraging Generative AI to Enhance Student Learning Outcomes: A Framework for
Educational Innovation
3. Rationale and Background
Artificial intelligence (AI) is revolutionizing a number of sectors, most notably higher education,
by bringing new techniques for enhancing teaching strategies and educational opportunities.
Generative artificial intelligence holds special promise for enhancing student engagement and
comprehension because of its ability to produce content, create engaging learning environments,
and adapt to user input.
In order to promote student engagement, improve problem-solving skills, and maintain
knowledge retention, this study aims to investigate the efficient application of generative
artificial intelligence in educational settings.
Although artificial intelligence has the power to change the way education is practiced, problems
of over-reliance on AI-generated material, ethics, and digital literacy disparities that must be
addressed limit its impact.
This study on the early adopters of generative artificial intelligence in educational environments
aims to offer best practices and recommendations on how to use AI to enhance student learning
outcomes. The results will assist in developing a framework for using generative artificial
intelligence in higher education, so ensuring its ethical and efficient adoption.
Research Questions:
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1. How are we using generative AI today to assist their learning?
2. What are the main advantages and disadvantages of integrating generative AI into pedagogical
practice?
3. How might generative artificial intelligence improve post-secondary education student
involvement, knowledge, and learning?
Major Approach
To get a whole picture of the effect of artificial intelligence on learning, this study will use a
mixture of methods combining qualitative and quantitative research. The combination of the two
strategies will enable a fair investigation of the efficacy of artificial intelligence, challenges
experienced, and the ethical concerns raised.
Quantitative Research:
• Teachers and students will be requested in questionnaires to identify the level of artificial
intelligence usage in schools.
• Statistical analysis will be used to measure the correlation between AI-assisted learning
and student performance.
• Experimental studies can be designed where students work with AI-based tools, and their
learning can be gauged by using pre- and post-tests.
Qualitative Research:
• There shall be detailed interviews with teachers, artificial intelligence professionals, and
students in order to explore their perception, experience, and challenge toward the use of
AI in instruction.
• Case studies will be built to analyze real-world applications of generative AI in schools.
• Following thematic analysis, we will determine the most important themes concerning the
potential of AI in improving learning achievements and participation.
Framework Development:
• Findings from both methods will be synthesized to develop a structured framework for
implementation by educators and education institutions in the successful integration of
generative AI in their pedagogy.
• Best practices, ethical guidelines, and policy suggestions for the responsible use of AI in
education will all be included in this framework.
By combining these methods, the study will offer contextual data and empirical results, offering a
fair evaluation of how generative AI affects students' academic performance.
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Significance of the Research
This study will advance AI research in education in theoretical, practical, and ethical ways.
1. Expanding Education's Understanding of AI:
• By investigating how generative artificial intelligence can improve knowledge retention,
boost student engagement, and personalize learning experiences.
• This study seeks to fill gaps in our understanding of the practical applications, limitations,
and effectiveness of AI in diverse educational contexts.
2. Offering Useful Advice to Teachers:
• The study will provide teachers with useful advice and techniques on how to incorporate
AI-based tools into their teaching practices most successfully.
• It will show excellence in applying AI to aid multiple learning modes, from adaptive
learning to real-time feedback loops.
• Teacher training recommendations will be provided in order to assure AI's maximum
capability and minimal difficulty in terms of over-reliance on AI-generated content.
3. Addressing Ethical Considerations and Risks:
• The application of AI in education raises ethical issues, such as bias in AI models, privacy
risks to data, and academic integrity issues.
• This study will evaluate these risks and recommend standards for ethical application of AI
to promote fairness, openness, and responsibility in AI-based education. The study would
also evaluate students' digital literacy and preparedness for utilizing AI-based learning
resources.
In besides enhancing educational innovation, this research will guarantee that the application of
artificial intelligence complies with ethical considerations while effectively fostering student
growth by looking at these factors.
4. Study Aims and Objectives
The main aim of this study is to explore how generative artificial intelligence can be used to
enhance learning outcomes for students, and how to create a systematic approach to its effective
and ethical application in higher education. Based on a review of the impact of generative AI on
personalized learning, student engagement, and knowledge retention, this study intends to bridge
the gap between AI technology and pedagogy. This study also intends to provide evidence-based
recommendations to teachers, institutions, and policymakers regarding the optimization of AI
potential, while minimizing the associated risks.
Objectives:
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1. Identify the ways in which students use generative AI tools for learning.
• Describe the various uses of generative AI in student learning, including automated content
generation, individualized tutoring, and interactive assessment.
• Evaluate students' perceptions, motivations, and behaviors toward the use of AI-based tools
in their assignments.
• Explain the degree to which students use AI for learning, problem-solving, and skill
acquisition.
2. Assess the impact of generative AI on student engagement and comprehension.
• Investigate if AI content aids students' comprehension of intricate topics through adaptive
learning experiences.
• Evaluate the impact of AI-enhanced learning techniques on academic performance,
creative abilities, and reasoning.
• Recognize possible enhancements in student motivation, engagement, and collaboration as
a result of incorporating AI into academic curricula.
3. Investigate the ethical challenges and limitations of AI in education.
• Discuss academic honesty, plagiarism, and excessive use of AI-generated material.
• Evaluate bias issues in AI-created learning resources and its effect on inclusive education.
• Examine data privacy, security, and transparency concerns surrounding AI-based learning
platforms.
• Assess levels of digital literacy and readiness for AI-driven learning environments among
students and teachers.
4. Develop a framework for educators to integrate AI into teaching practices effectively.
• Recommend optimal methodologies for utilizing artificial intelligence within higher
education contexts, ensuring that it serves to enhance rather than supplant human-led
teaching.
• Establish ethical guidelines and action plans to offer responsible adoption of AI in
education.
• Design a scaffolded approach through which teachers can employ AI tools to generate
content, grade assignments, and assist students.
• Advise institutional policy that supports AI literacy, the responsible use of AI, and the
sustainable incorporation of AI into educational environments.
5. Literature Review
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Another fascinating field of research that has the potential to fundamentally change the way that
teaching and learning are conducted is the expanding nexus of AI technologies and education.
According to certain studies, artificial intelligence has broad implications for improving learner
interaction, efficiency, and personalization.
Personalized Learning
The ability of artificial intelligence to provide individualized learning experiences is among its
most significant benefits in the field of education. The one-size-fits-all methodology of
traditional educational models may not take into account each learner's unique learning
preferences, strengths, or shortcomings. Large datasets may be analyzed by AI-powered software
to tailor instructional materials to each learner's particular requirements. According to Luckin
(2018), AI systems may be able to adjust the type of educational content, instructional speed, and
degree of complexity in real time based on a learner's performance. To assist learners in
overcoming specific obstacles, adaptive learning software, for instance, can provide targeted
practice, support content recommendations, or personalized guidance.
Interactive Learning
The way students engage with educational content is being completely transformed by generative
AI technologies, which include tools like AI-powered simulations and intelligent tutoring systems.
By facilitating interactive learning, Holmes et al. (2021) highlight how generative AI can improve
student engagement. Through virtual environments, AI-powered dialogue systems, or simulations,
these technologies enable immersive experiences where students can actively interact with the
course material. In disciplines like science, math, or history, for instance, AI-powered simulations
allow students to engage in real-world situations or conduct experiments in virtual labs that would
be challenging or impossible in a conventional classroom.
Ethical Considerations
Generative AI technologies like intelligent tutoring systems and AI-facilitated simulations are
redefining the modalities of student interaction with learning material. Holmes et al. (2021) note
the importance of generative AI in facilitating student engagement through interactive learning
experiences. Generative AI technologies offer the promise of engaging learning, where active
interaction with learning material is possible through mediums like simulations, virtual worlds, or
AI-supported dialogue systems. For example, AI-facilitated simulations in subjects like science,
mathematics, and history enable students to perform experiments in virtual labs or participate in
real-life situations that would be difficult or impossible in a traditional classroom environment.
Future Directions for Research
This research study will draw on current questions while simultaneously addressing a few gaps in
the understanding of the wider effects of artificial intelligence in higher education. One prominent
area that requires further examination is how students engage generative AI in an educational
setting. How, for instance, do students assess AI tools that customize their learning or offer instant
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feedback? What impact does AI have on students' academic performance, motivation, and
engagement in a variety of learning environments? likewise a comprehensive evaluation of the
potential drawbacks of an over-reliance on AI is necessary, including how it may affect students'
capacity for creativity, problem-solving, and social interaction.
6. Suggested Research Techniques
To gather both qualitative and quantitative insights, a mixed-methods research design will be used:
PHASE RESEARCH OBJECTIVE DATA DATA
METHOD COLLECTION ANALYSIS
TECHNIQUES
PHASE Survey Study Analyze university Online surveys Statistical analysis:
1 students' current use distributed to Descriptive
of generative AI in students at multiple statistics,
learning, focusing on universities. correlations.
frequency, tools, and
perceived benefits.
PHASE Case Studies Examine real-life In-depth interviews Thematic analysis:
2 examples of students with students and Identify patterns
and educators using educators using AI and themes from
generative AI to in classrooms. interviews.
assess its impact on
engagement,
motivation, and
learning outcomes.
PHASE Expert Gather insights from Semi-structured Qualitative
3 Interviews educators and AI interviews with a analysis: Thematic
specialists on best range of AI experts analysis for expert
practices, challenges, and educators. responses.
and the future of AI
in education.
PHASE Framework Synthesize the Compilation of Synthesis of
4 Development research findings to insights from qualitative and
create a practical surveys, case quantitative data to
framework for studies, and develop a
integrating interviews. framework.
generative AI into
higher education.
Table 1 Research Method
Data Analysis Techniques Overview:
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• Thematic Analysis: To analyze qualitative data (from interviews of students, case studies,
and expert interviews) to identify key themes and patterns about the use of AI for learning,
including engagement, outcomes, and difficulties.
• Statistical Analysis: The quantitative data collected through surveys will be subjected to a
variety of statistical techniques, such as regression models, in order to highlight the
connections between the use of AI and learning outcomes, such as performance and student
satisfaction.
7. Advantages, Contributions, and Uses of the Results
By offering useful information to students, teachers, and policymakers, this study seeks to close
the gap between AI technology and higher education.
Key Contributions:
• Instructional Effect: Determining ways in which students may use AI to learn.
• Pedagogical Support: Developing teacher guidelines on how to incorporate AI.
• Ethical concerns: Focusing on issues like bias in AI, digital literacy and ethical use of
AI.
• Policy-making: Guiding schools and policymakers on methods of implementing AI.
8. References/Bibliography
I. Aoun, J.E. (2017) Robot-proof: Higher education in the age of artificial intelligence.
Cambridge, MA: MIT Press. Available at: https://mitpress.mit.edu/9780262535977/robot-proof/ .
mitpress.mit.edu
II. Ferguson, R. and Buckingham Shum, S. (2019) ‘The promise and perils of AI in learning
analytics’, Learning, Media and Technology, 44(3), pp. 301–315. Available at:
https://doi.org/10.1080/17439884.2019.1623250 .
III. Holmes, W., Bialik, M. and Fadel, C. (2019) Artificial intelligence in education: promises and
implications for teaching and learning. Boston: Center for Curriculum Redesign. Available at:
https://curriculumredesign.org/wp-content/uploads/AIED-Book-Excerpt-CCR.pdf .
curriculumredesign.org
IV. Luckin, R. (2018) Machine learning and human intelligence: The future of education for the
21st century. London: UCL Institute of Education Press. Available at:
https://discovery.ucl.ac.uk/id/eprint/10061395/ .
V. Passey, D. (2021) AI and machine learning in education: Implications for policy and practice.
Cham: Springer. Available at: https://doi.org/10.1007/978-3-030-81489-2 .
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VI. Selwyn, N. (2019) Should robots replace teachers? AI and the future of education. Cambridge:
Polity Press. Available at: https://www.politybooks.com/bookdetail?book_slug=should-robots-
replace-teachers--ai-and-the-future-of-education--9781509528965 .
VII. Wang, Y. and Murugesan, R. (2022) ‘The role of generative AI in higher education: A
systematic review’, Journal of Educational Technology & Society, 25(2), pp. 45–62. Available at:
https://www.j-ets.net/ETS/journals/25_2/ETS_25_2_05.pdf .
VIII. Williamson, B. and Eynon, R. (2020) ‘Historical threads, missing links, and future directions
in AI in education’, Learning, Media and Technology, 45(3), pp. 223–235. Available at:
https://doi.org/10.1080/17439884.2020.1798995 .tandfonline.com+1link.springer.com+1
IX. Zawacki-Richter, O., Marín, V.I., Bond, M. and Gouverneur, F. (2019) ‘Systematic review of
research on artificial intelligence applications in higher education – where are the educators?’,
International Journal of Educational Technology in Higher Education, 16(39), pp. 1–27.
Available at: https://educationaltechnologyjournal.springeropen.com/articles/10.1186/s41239-
019-0171-0 .
9. Career Aspirations/Motivation for the Desire to Gain a Higher Degree
Qualification
Pursuing this research at the Masters By Research level aligns with my long-term goal of
contributing to the evolving landscape of AI in education. With a background in web development
and technology-enhanced learning, I am particularly interested in exploring how AI-driven tools
can empower students and educators. A MRes from RGU will equip me with the skills to lead
research-driven educational innovation and contribute to the development of AI-integrated
learning solutions.
10. Source of Funding to Support Study
With the possibility of applying for research grants or scholarships related to my area of study, I
plan to finance my own research. I'm also willing to look into funding options offered by Robert
Gordon University and outside research organizations.
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