Course Name: Using Generative AI in Research
Time and place: University of Jyväskylä, late August 2025 (exact dates to be
determined)
Learning goal and objectives: This course aims to provide doctoral students with
hands-on experience and practical skills in leveraging Large Language Models
(LLMs) for various aspects of academic research. The course is designed to
accommodate both beginners and those with some AI experience.
By the end of the course, students will be able to:
• Understand the fundamental principles, capabilities, and limitations of
generative AI and LLMs
• Apply appropriate AI tools ethically and effectively across different
research phases
• Develop critical skills in prompt engineering for research-specific tasks
• Implement AI-assisted workflows for literature reviews and academic
writing
• Utilize AI tools for both quantitative and qualitative data analysis
• Evaluate the ethical and legal implications of AI use in academic contexts
• Set up and use local LLM installations for research privacy and
customization
• Creatively and critically evaluate when and how AI tools can enhance
specific research processes
Instruction and examination: The course is structured as a two-day intensive
workshop with additional pre- and post-course assignments. Each day runs from 9:15
to 17:00 and includes:
• Interactive lectures on key topics
• Guided hands-on exercises using various AI platforms (e.g., Claude, GPT-4,
and Gemini)
• Small group discussions and collaborative problem-solving
• Practical demonstrations of research-specific AI applications
The course will cover practical applications of AI such as:
• Visualization of regression results
• Full analysis of datasets
• Grounded theory analysis using interview data
• Coding of large numbers of survey responses
• Improving academic writing
• Ideating discussions sections
• Writing feedback to students
• Grading exams and assignments
• Automating work with coding agent Cline and using GitHub Copilot
Practical activities will include:
• Setting up and using Ollama to run open-source LLMs locally (such as
Gemma, Llama, and DeepSeek)
• Creating effective research-oriented prompts for different AI models
• Accessing and utilizing API endpoints with provided API keys to complete
practical research tasks
• Testing special-purpose AI tools (e.g. Deep Research)
• Comparative analysis of different AI models for specific research tasks
• Guided programming exercises in Python (no prior programming experience
required)
A course dinner will be arranged between the two workshop days to facilitate
networking and informal discussion.
Assessment will be based on:
• Pre-course assignment: Reflective essay (1000-1500 words) based on
provided readings about AI in academic research (20%)
• Active participation during the workshop (40%)
• Post-course assignment: Critical reflection on potential applications of AI in
the student’s field or a small practical experiment with an AI tool of choice,
documented with screenshots and analysis (40%)
Accepted participants will receive a reading package containing articles, book
chapters, and videos on using AI that should be completed before the course.
Credits: 3 ECTS
Grading: 1-5 based on pre-course assignment, participation, and post-course
assignment.
Prerequisites:
• Basic computer literacy
• Interest in integrating AI tools into research processes
• Background in research methods is beneficial but not required
• No prior experience with AI tools is necessary – the course is designed for
both beginners and more advanced users
• Computer programming knowledge is not required
Admittance: Maximum 25 students will be admitted to the course. Applications
should include a brief description (max. 300 words) of the student’s research interests
and why they want to participate in the course. Application deadline is [date].
Accepted students will be notified by [date].
Instructors:
Dr. Mikko Rönkkö is an Associate Professor of Entrepreneurship at Jyväskylä
University School of Business and Economics (JSBE) and a docent at Aalto
University School of Science. He has published extensively on research methods in
both methodology journals and applied journals in management and information
systems. Dr. Rönkkö serves as an Associate Editor for Organizational Research
Methods and is on the editorial board of Entrepreneurship Theory and Practice. He
runs a research methods-focused YouTube channel with over 10,000 subscribers and
maintains expertise in statistical methods, research design, and entrepreneurship. Dr.
Rönkkö brings practical experience as a former entrepreneur to his teaching and
research.
Dr. Ville Heilala is a University Researcher at the University of Jyväskylä
specializing in computational methods, artificial intelligence, and machine learning
applications in human sciences. With a PhD in computer science and background in
education, his research focuses on leveraging technology to enhance human
capabilities. Dr. Heilala’s interdisciplinary expertise spans computational human
sciences, AI in education, multimodal learning analytics, and computational
psychometrics. He supervises doctoral researchers across diverse fields including
pilot training assessment, virtual reality learning, and AI literacy among educators,
bringing practical insights on AI implementation across various research domains.
Course coordinator and contact information: TBA