| ARC – The AI² Research Club A research-oriented community for students interested in Artificial Intelligence, applied Machine Learning, scientific literature, and responsible research practice. | AI² THE RESEARCH
CLUB AT FEAA |
ARC – The AI² Research Club is a continuous, non-graded extracurricular academic initiative within the Faculty of Economics and Business Administration at Alexandru Ioan Cuza University of Iași.
ARC is built around experiential learning and research practice. The club supports students who want to understand how Artificial Intelligence and Machine Learning are used in real projects, how scientific research is designed and evaluated, and how contemporary AI tools can support academic work without replacing verification, reasoning, or intellectual responsibility.
Working language. Discussions are primarily in Romanian. Papers, experiments, technical resources, and most educational materials are in English, reflecting the language commonly used in international AI and research literature.
ARC has two complementary participation formats. Some activities are open to the wider FEAA community, while the restricted group supports members who want to take part in recurring meetings, project work, and longer-term research development.
SESIUNI DESCHISE Open Sessions Open sessions are available to the broader FEAA community and may include workshops, Responsible AI sessions, research-craft activities, invited speakers, and other events intended for students who want to participate without joining the restricted working group. | GRUP RESTRÂNS · UP TO 30 MEMBERS Restricted Working Group The restricted group is a selective cohort whose members participate in recurring meetings, deeper mentorship, project development, individual research work, and periodic progress checkpoints. Membership can continue across semesters and academic years. |
ARC activities move between technical work, applied research, scientific literature, and the practical questions that appear when AI becomes part of a research workflow.
AI Foundations and Core Machine Learning Machine Learning fundamentals, model intuition, evaluation basics, classical ML methods, Neural Network essentials, dataset preparation, and practical experimentation. | Applied AI/ML by Domain Projects and research ideas in areas such as cybersecurity, sports analytics, economics, finance, marketing, operations, education, and other domains represented within the community. |
Research Craft and Scientometrics Research-question formulation, literature searching, paper structure, critical reading, literature reviews, bibliometric and scientometric methods, and research synthesis. | Responsible AI and Research Workflows Prompting, verification, reproducibility, literature discovery, code assistance, data analysis, and practical ways of using LLM tools while keeping the researcher responsible for the work. |
ARC is structured around recurring activities, not a fixed course syllabus. Members may continue across semesters, and the programme changes according to ongoing projects, research interests, and the needs of the group.
Expect a mix of discussion and practical work: less passive lecture, more “we actually try it and see what breaks.”
Paper Deconstructed Guided discussions examining a paper's problem, method, evaluation, limitations, assumptions, and practical relevance. | The Forge Floor Short project or research pitches, followed by discussion of questions, datasets, baselines, feasibility, and collaboration. | Skill Workshops Practical sessions in Machine Learning, exploratory data analysis, NLP, evaluation, bibliometrics, scientometrics, and research writing. |
Responsible AI Sessions Prompting, debugging, literature discovery, verification, academic integrity, reproducibility, and avoiding fabricated references or data. | Research Spotlight Guests discuss the decisions, false starts, and compromises behind research, not only the final publication. | Project Checkpoints and Demos Members present progress, obstacles, intermediate results, demonstrations, and next steps. Useful negative results are welcome too. |
USUALLY Saturdays outside class time | This gives us enough time to work, experiment, and discuss without immediately running to another class while grabbing a cappuccino... or a tea. :) Meetings may be weekly, biweekly, or scheduled every few weeks depending on the semester plan, project stage, and current track activities. Meetings are normally held on site, with practical details announced through ARC's official communication channels. |
Applications are open primarily to students of the Faculty of Economics and Business Administration. Students from all years of study are welcome.
ARC has a strong connection with programmes such as Informatică Economică, Software Development and Business Information Systems, and Sisteme Informaționale pentru Afaceri, but membership is not limited to these programmes.
Students from Data Mining, Statistică Economică și Data Science, and other Bachelor, Master, and PhD programmes within FEAA are welcome if they have a genuine interest in Artificial Intelligence, Machine Learning, NLP, data analysis, research methods, or scientific-literature analysis.
Students in their final Bachelor or Master year can also apply. You may even decide later that you want to return as an alumni mentor. :)
RESTRICTED GROUP Selection and membership Submitting an application does not automatically guarantee admission. | Admission considers: - alignment with ARC's research and learning objectives;
- willingness to follow academic-integrity and responsible-AI principles;
- commitment to consistent participation;
- collaborative attitude and readiness to contribute to the community.
Students who prefer public activities may follow ARC through its open sessions and announcements. |
| 06 | Responsible AI and academic integrity | |
ARC follows academic-integrity standards. AI tools may be used for support, including brainstorming, language clarification, code assistance, debugging, literature discovery, and data-analysis workflows, but generated outputs must be checked by the person using them.
Fabricated citations, invented data, unsupported claims, and undisclosed misuse are not acceptable.
This is not a club where you learn how to trick the academic world into not detecting AI-generated content.
ARC is currently coordinated by doctoral researchers within the FEAA community. You can contact us through institutional Teams or by email if you have questions about membership, activities, research ideas, or upcoming sessions.
Data processing notice. Information submitted through ARC membership forms is collected for membership selection and club administration and is handled internally by the coordinators. You may request deletion of your information at any time.
