Introduction to Research Methods
RES799 - Graduate Course
Fall 2025
Course Description
The course teaches research methods applicable to scientific research in general, and AI research in particular. It covers various topics including quantitative, qualitative, and mixed methods, measurement and metrics in experimental research, critical appraisal and peer review, public communication, reproducibility and open science, and ethical issues in AI research. Students will gain knowledge in selecting, evaluating, collecting and sharing data and suitable research methods to address specific research questions. After completing the course, students will have the skills to develop a full research methodology that is rigorous and ethical.
Course Team
Dr. Hanan Aldarmaki - Course Coordinator
Assistant Professor, NLP & Speech @ MBZUAI
Assistant Professor, NLP & Speech @ MBZUAI
Learning Objectives
- Formulate a research question and design an experimental study by identifying suitable research methods and instruments.
- Conduct a literature search and identify related work.
- Identify and discuss the limitations of existing research methods through peer review.
- Communicate research findings effectively to fellow researchers through written and verbal communication.
- Understand and discuss research ethics, academic integrity principles, and the ethical implications of AI research.
Teaching Plan
| Week | Topics | Assessment |
|---|---|---|
| 1 | Lecture 1: Introduction / Philosophical
Foundations, the scientific
method.
Lecture 2: Quantitative Research Methods, Principles, Experimental design, Sampling methods, Descriptive Statistics. |
N/A |
| 2 | Lecture 3: Quantitative Research Methods
(cont.), Statistical inference, Hypothesis testing,
Confidence intervals, Effect size,
Correlation/Regression
Lecture 4: Quantitative Research Methods advanced topics: longitudinal and crosssectional studies, multi-variate, more advanced visualization |
Homework 1 |
| 3 | Lecture 5: Qualitative research principles
and approaches, common types of
qualitative studies, participant selection in
qualitative research, examples from AI
research
Lecture 6: Qualitative research (cont.), qualitative data analysis methods, interpreting and presenting qualitative findings, mixed-methods research |
N/A |
| 4 | Lecture 7: Academic integrity, MBZUAI
library resources, Literature search.
Lecture 8: Research communication & Paper writing (best practices). |
Homework 2 |
| 5 | Lecture 9: Replication and Reproducibility
in Research.
Lecture 10: Open science and FAIR data. |
N/A |
| 6 | Lecture 11: The publication process.
Lecture 12: Peer review exercise. |
Homework 3 |
| 7 | Lecture 13: Core ethical principles,
Research ethics, informed consent,
confidentiality, IRB process, Ethical issues
in AI and digital research.
Lecture 14: AI ethics (guest lecture) |
Quiz |
Grading
Attendance
14%
Participation
14%
Assignments
60%
Quiz
12%