50 AI and Education Dissertation  Topics for Students to Explore in 2026

50 AI and Education Dissertation  Topics for Students to Explore in 2026

Artificial intelligence is changing education faster than ever, from personalized learning and AI tutors to automated feedback, smart assessment, and AI-powered research tools. But this rapid growth also brings important questions: Is AI helping students learn deeply or simply complete tasks faster? Can traditional assessments still measure genuine learning? What happens to critical thinking, creativity, privacy, and human interaction when AI becomes part of everyday education? 

For students looking for a relevant dissertation topic in 2026, these emerging challenges offer valuable research opportunities. 

50 AI and Education  Research Topics to Explore in 2026 

Learning Outcomes & Academic Achievement

• AI-Personalised Learning and Academic Performance: A Comparative Study

• Long-Term Knowledge Retention in AI-Tutored and Traditionally Taught Students

• Evaluating the Accuracy and Curriculum Alignment of AI-Generated Learning Content

• Does AI-Supported Learning Narrow or Widen the Achievement Gap Between High- and Low-Performing Students?

• The Effect of AI Writing-Feedback Tools on Students’ Essay Quality

• AI in STEM Education: Comparing Conceptual Understanding and Procedural Skills in Mathematics

• AI-Supported Exam Revision vs Traditional Revision: A Comparison of Academic Outcomes

• Measuring Learning Depth in AI-Supported Classrooms: A Mixed-Methods Study

This section explores how AI affects students’ academic performance, knowledge retention, understanding, and learning depth. It also examines whether AI-supported learning produces better outcomes than traditional approaches. 

Critical Thinking & Higher-Order Skills

• Generative AI Use and Students’ Critical Thinking Skills

• AI-Assisted Problem Solving and Students’ Independent Reasoning Ability

• Cognitive Offloading Through Generative AI and Its Effect on Higher-Order Thinking

• AI Use and Students’ Ability to Evaluate Evidence and Academic Sources

• Can Verifying AI Answers Improve Students’ Critical Thinking Skills?

• Generative AI and Students’ Analytical Reasoning in Higher Education

• AI Dependence and Students’ Metacognitive Skills

• Generative AI and Creative Thinking: Support for Creativity or Replacement of the Creative Process?

• AI-Assisted Decision-Making and Students’ Ability to Make Independent Academic Judgements

• The Effect of AI Assistance on Students’ Intellectual Effort and Learning Engagement

These topics focus on how AI influences critical thinking, creativity, reasoning, problem-solving, and independent judgement. They explore whether AI strengthens these skills or encourages students to rely on automated answers. 

AI and Assessment

• Generative AI and the Validity of Traditional University Assessments

• AI-Resistant Assessment: Which Assessment Methods Best Measure Genuine Learning?

• Process-Based Assessment in the Age of Generative AI

• AI Detection Tools and the Accuracy of Academic Misconduct Decisions

• AI-Assisted Assignments and the Reliability of Grades as Evidence of Student Learning

• Oral Assessment as a Response to Generative AI-Assisted Coursework

• Student Disclosure of AI Use and Academic Integrity

• Redesigning University Assessment to Measure Thinking Rather Than AI-Generated Output

• Teacher Judgement vs AI Detection Tools in Identifying AI-Assisted Academic Work

• Generative AI and the Future of Written Assessment in Higher Education

This section examines how generative AI is changing traditional assessment and academic integrity. It covers AI detection, assessment validity, genuine learning, and new ways of measuring students’ abilities. 

AI, Inclusion & Special Education

• AI-Powered Accessibility Tools and Learning Outcomes for Students With Disabilities

• Generative AI and Inclusive Education: Can AI Reduce Barriers to Learning?

• AI-Based Personalised Support for Students With Learning Difficulties

• AI Assistive Technologies and Student Independence in Higher Education

• Accessibility and Bias in AI Educational Tools for Students With Disabilities

• AI-Powered Speech and Communication Tools for Students With Additional Learning Needs

These topics explore how AI can support students with disabilities and different learning needs. They also consider accessibility, personalised assistance, communication support, and possible bias in educational AI tools. 

Human Connection & Student Well-Being

• AI Tutors vs Human Teachers: What Happens to Student–Teacher Relationships?

• AI Companions in Education: Support for Students or Replacement for Human Interaction?

• Generative AI and Student Academic Anxiety: Does Instant Assistance Reduce or Increase Stress?

• AI-Supported Learning and Student Motivation: Engagement or Passive Learning?

• The Human Connection Problem: Can AI-Supported Education Maintain Meaningful Social Learning?

This section examines the human and emotional side of AI-supported education. It explores student–teacher relationships, motivation, anxiety, social learning, and the possible effects of AI dependence. 

AI, Curriculum & Future Skills

• Redesigning Educational Curricula for an AI-Driven Era

• Human Skills in the Age of AI: Identifying the Competencies Students Still Need to Develop

• Generative AI and the Changing Value of Writing, Research and Problem-Solving Skills

These topics focus on how education and curricula need to change as AI becomes more common. They explore essential human skills, changing academic abilities, and preparation for an AI-driven future. 

AI, Research & Academic Knowledge

• Generative AI and Students’ Ability to Identify Research Gaps in Academic Literature

• AI-Assisted Literature Searching and the Quality of Students’ Academic Research

This section looks at how AI is changing the way students conduct academic research and work with literature. It covers research gaps, literature searching, source discovery, and the quality of academic research. 

AI, Information & Misinformation

• Generative AI and Students’ Ability to Recognise Misinformation in Academic Content

• AI Search Tools and Changes in Students’ Academic Information-Seeking Behaviour

These topics examine how students find, understand, and verify information when AI-generated content is widely available. They focus on misinformation, information-seeking behaviour, and students’ ability to judge reliable information. 

AI, Privacy & Data Protection

• Student Data Privacy in AI-Powered Education: Awareness, Risks and Institutional Responsibility

• AI-Based Student Monitoring and the Tension Between Educational Support and Privacy

This section explores concerns about student data in AI-powered education. It covers privacy, data collection, monitoring, surveillance, consent, and the responsibility of educational institutions. 

AI, Employability & Future Education

• AI Readiness Among University Students: Preparing Graduates for AI-Driven Workplaces

• The AI Skills Gap in Higher Education: Are Universities Teaching the Skills Employers Now Need?

These topics investigate whether students are developing the AI skills needed for modern workplaces. They also examine the changing skills gap and how universities can prepare graduates for an increasingly AI-driven labour market. 

AI is becoming an important part of modern education, creating both new opportunities and serious challenges for students, teachers, and institutions. From improving personalised learning to changing assessment, research, and future skill requirements, its influence continues to grow. However, effective use of AI requires careful attention to learning quality, independent thinking, academic integrity, privacy, fairness, and human connection. 

The 50 topics discussed above provide a range of research directions for students who want to explore these changes in 2026. Choosing a focused topic with sufficient academic literature, relevant data, and clear research objectives can help create a meaningful and valuable dissertation.