Course unit for PhD students
Aim
The course will provide participants with an overview of AI tools that can be effectively used in the production of content in the academic field. The program consists of different parts. The first part is aimed at providing a theoretical and technical framework on the types of AI and their main areas of application. The second part focuses on the concrete analysis of available tools and their primary uses, with also a focus on integrity, repeatability, reproducibility, and replicability issues. The third part explores the bidirectional relationship between AI and scientific disciplines, with a focus on the impact of AI techniques within specific fields and the transformative nature of their adoption.
By the end of the course, participants will have acquired knowledge about the main types of AI and the underlying technologies, as well as skills related to the functioning and potential of the main AI tools available on the World Wide Web, and their possible application in various fields. Participants will also acquire awareness about the steps they should take to ensure rigour in research processes and to maintain the repeatability; reproducibility and replicability of their research are also analysed. Through guided cowriting practices and self-assessment rubrics, doctoral students develop critical skills to improve the quality, coherence, and methodological rigor of research articles. The approach aims to promote an informed relationship between the human author, the scientific process, and AI systems.
Course contents:
First Part (4h) - Introduction to Generative AI Topics covered:
- What is artificial intelligence
- Basic concepts of machine learning
- Generative AI and language models
Second Part (7h) - AI Tools and Their Possible Uses Topics covered:
- Idea generation, brainstorming, and definition of outline for content
- Not just ChatGPT and LLM: AI tools or AI-powered free or freemium tools for research
- Prompt engineering: guidelines and tools for generating effective prompts
- Risks of AI: examples of bad practices (images, scientific articles, etc.)
- Best Practices and guidelines: STM Publishers’ Association, COPE, ISMPP, etc.
- Research Integrity and Reproducibility in the Era of Generative AI
Third part (10h) - Relationship between AI and scientific disciplines covered:
- Introduction to the AI for Science landscape
- Series of seminar-type events on AI applications across various disciplines (AI for Medicine, AI for Bio/Chem/Life Sciences, AI for High Energy Physics, AI for Mathematics, AI for Industry
- Panel with speakers: the final debate
Assessment method: The assessment is based solely on the criterion of suitability/unsuitability. The first three modules include a single assessment test with multiple-choice questions.The test will be held in person in Bologna on 5 May at 11.00 a.m. in Aula A - Capecchi, Via del Guasto 3, Bologna.
Teaching modes: online lessons Ms Teams
Duration: 21 h + individual study activities
Available places: 100
Calendar:
An introduction to Artificial Intelligence and Machine Learning: 8 april (h. 15-17)
Generative AI and Language Models: 9 april (h. 15-17)
AI Tools and Their Possible Uses Topics covered: 13 and 15 april (h. 10-12)
Research Integrity: 20 april (h. 15-16)
Reproducibility Crysis and solutions: 20 aprile (h.16-18)
Introduction to the AI for Science landscape: 21 april (h.09-11)
Seminar-type events on AI applications across various disciplines and panel with speakers: 21 april (h.11-13), 22 april (h.11-13), 23 april (h.09-11), 28 april (h.11-13)
Language of the course: English
Trainers: Prof. Paolo Torroni, Prof. Daniele Bonacorsi
Doctoral credits (DCs): 2.5
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30 PhD students out of the students who would pass the first 3 modules will have the opportunity to attend a further 4-hour module. This further module will be composed of classroom-taught lessons and accounts for 0.5 doctoral credits.
PhD students who are eligible for this opportunity will receive an email from the relevant Offices. For further details, please read below.
Scientific Writing and Self-Assessment with AI Topics covered:
- How AI can support the different stages of article development: from idea generation and structuring, to stylistic revision and scientific coherence checking, while maintaining clear authorial responsibility
- self-assessment practices (rubrics, checklists, version comparison) to enhance argumentation, methodological rigor, and transparency
- Impact on text quality, revision time, and the development of critical awareness.
Assessment method: The assessment is based solely on the criterion of suitability/unsuitability. The fourth module includes an assessment of the course in the last lesson through practical exercises and supervised self-assessment using AI tools.
Teaching modes: in presence lessons aula I Belmeloro, via Beniamino Andreatta 8, Bologna.
Duration: 4 h + individual study activities
Available places: 30
Calendar: 21 and 26 may h. 15 - 17
Language of the course: English
Trainers: Prof.ssa Chiara Panciroli
Doctoral credits (DCs): 0.5