Università per Stranieri di Perugia
Search the site

AIDI - AI for Learning in Italian and Dialogue in Italian

Teaching and learning Italian as a second/foreign language with AI in collaboration with the IUL Online University

The research project aims to implement functionalities based on Artificial Intelligence (AI) in the online Italian courses of the University for Foreigners of Perugia, in order to enhance language practice, provide simplified access to information, personalized learning, and student guidance.

The research project seeks to integrate generative AI technology within online Italian language courses, with the goal of promoting effective learning and a personalized educational experience. Strengthening online courses with AI also provides an opportunity to contribute to the development of testing and assessment tools that are accurate, efficient, and impartial. The experimental phase, involving learners of various backgrounds and nationalities, will be of crucial importance: by collecting user feedback, monitoring frequency and usage time, and tracking improvements in skills development, it will be possible to assess the effectiveness of activities and their level of acceptance. At the same time, limitations, potential errors, and the correct use of such tools will also be analyzed, ensuring opportunities for improvement in the research project.

The structuring of materials will be preceded by a study aimed at configuring an accessible and inclusive online learning environment. The analysis of the principles of Universal Design for Learning (UDL) will play a crucial role, helping to ensure a universal, plural, and accessible teaching approach that values the differences and strengths of each student. Within this framework, the design of AI systems pursues the objective of providing multiple options for engagement, such as interactivity, collaboration, self-learning, multiple representations, and diversified ways for learners to express their skills. Reference to regulations and guidelines regarding the management of students with specific learning disorders and disabilities, both in Italy and at the European level, will be fundamental, aiming to guide the design and implementation phases of the courses in ways that promote learning for everyone as well as for each individual.

  • promote corpus linguistics and the creation, dissemination, and use of linguistic corpora in research and teaching
  • analyze the current state of the online Italian courses at the University for Foreigners of Perugia, identifying areas that could benefit from the implementation of AI systems
  • identify and design AI-enhanced language teaching activities that encourage language practice, access to information, and orientation for incoming students
  • design, develop, and integrate AI systems within the courses, in collaboration with IUL University
  • evaluate the impact of AI systems on students’ language learning, focusing on the effectiveness of personalized learning and the use of AI as a tool for automatic correction of errors in written production
  • explore students' perceptions regarding the use of AI systems to improve learning and simplify access to information, gathering expectations and feedback on their usage experiences

Letizia Cinganotto, researcher RtdB L-LIN/02
Roberto Dolci, associate professor L-LIN/02
Valentino Santucci, associate professor IINF-05/A
Simone Filippetti, PhD student (track in Linguistics and Language Teaching), XXXIX cycle
Giorgia Montanucci, PhD student (track in Linguistics and Language Teaching), XXXVIII cycle
Talia Sbardella, PhD student (track in Linguistics and Language Teaching), XXXVI cycle
Francesco Mugnai, Senior Developer, IUL Telematic University

Phases of the Research Project

The study will unfold over a period of about two years and will be structured in the following phases:

Phase 1: Preliminary study of the potential for AI implementation in online Italian courses
A preliminary analysis, also conducted based on the scientific literature relevant to the research, will make it possible to identify the areas where the integration of Artificial Intelligence-based tools is most effective in improving the language skills of Italian language students, thus contributing to the enhancement of activities already present in the Courses. In particular, attention will be focused on certain areas that are traditionally less represented in online courses, such as oral production, written production with instant feedback, and widespread tutoring.

Phase 2: Design and creation of AI systems to be integrated
Based on the study conducted in Phase 1, the design of the AI systems to be implemented will be carried out by AI experts and language teaching experts, also in collaboration with IUL University. An expert team will work on an interdisciplinary level to coordinate and create tools that are consistent with the methodological approaches of the Courses, the syllabuses of the University for Foreigners of Perugia, and the CEFR.

Phase 3: Integration of AI systems into online courses
In the integration phase, the AI systems will be made accessible through the Learning Management System where the course materials and e-tivities are deployed. Users will be able to use specific functions which, depending on the level and the requirements of the client, will be activated at the time the Course is delivered.

Phase 4: Testing AI systems with data collection and analysis
Based on the data collected during the testing phase, an analysis will be carried out aiming to answer the research questions comprehensively. The data will cover various aspects of usability, interaction, productivity, and the effectiveness of the AI-powered activities carried out by students.

Phase 5: From data analysis to improvement perspectives
Starting from an evidence-based analysis and the collection of learner data and questionnaires, the effectiveness of these systems will be assessed based on the results obtained. This approach will make it possible to identify strengths, limitations, and areas for improvement that will help to optimize the use of AI in online courses, improving the learning experience. The goal is to identify effective teaching strategies and guidelines for the use of AI as a supplementary strategic tool in language education, taking into account any limitations or emerging issues.

Methodology

The research adopts a methodological approach that combines both qualitative and quantitative methods, in order to carry out an in-depth investigation concerning the research questions. Studies, surveys, and data analyses will be conducted within the online courses offered, involving students from a metacognitive perspective. Data will be collected and analyzed using a mixed methodology, with the aim of providing comprehensive answers to the project's research questions and, potentially, improving the AI systems themselves through a continuous improvement approach.

Tools

  • AI systems and models based on diversified, interconnected, cloud-based technologies
  • Learning Management Systems
  • digital tools enhanced with AI technologies

  • Cinganotto, L., Montanucci G. (2024). Artificial Intelligence and Language Learning: A Case Study on the Online Italian Language and Culture Courses of the University for Foreigners of Perugia, Status Quaestionis, 1/2024
  • Cinganotto, L. (2024). Linguistic and Multilingual Skills in the Age of Artificial Intelligence, in Ricucci R., Rosa A. (eds.), TEACHING FOR SKILLS AND EDUCATIONAL HORIZONS - The Lifelong Learning Perspective, PensaMultimedia
  • Cinganotto, L. (2024). Language Testing Online: Experiments on the Italian Language, Italiano LinguaDue, issue 1, year 2024
  • Cinganotto, L., Montanucci, G. (2024). Exploring the Integration of Artificial Intelligence in Online Language Learning: A Case Example on Italian as a Foreign Language, in Greco, S., Cinganotto, L. (eds.), Innovation in Education for Deeper Learning, INDIRE-IUL Press, Series "Quaderni" - Florence
  • Cinganotto, L. (2018). Language Learning Technologies: A Study. Humanising Language Teaching, 20(6)
  • Cinganotto, L., &Cuccurullo, D. (2019). Techno-CLIL. Doing CLIL Digitally, Turin, Loescher
  • Cinganotto, L. (2021). Learning English with Artificial Intelligence, Brescia, La Scuola Sei
  • Cinganotto, L. (2022). Artificial Intelligence for Language Learning. In Conference Proceedings. Innovation in Language Learning 2022
  • Cinganotto, L., & Greco, S. (2023). Re-thinking Education in the Age of AI, Journal of e-Learning and Knowledge Society, 19(3), I-IV
  • Montanucci, G., & Peconi, A. (2023). Artificial Intelligence and Special Educational Needs: Exploring the Potential for Personalized and Inclusive Learning. In Conference Proceedings. Innovation in Language Learning 2023
  • Calicchio, C., Montanucci, G., & Peconi, A. (2023). Fostering Educational Sustainability Through Language Teachers' Training: Exploring Metacognitive Perspectives and Technological Integration. In Conference Proceedings. ICERI 2023
  • Calicchio, C., Montanucci, G., & Peconi, A. (2023). Exploring The Potential Of Memes In Language Teaching: Enhancing Pragmatic Skills And Embracing Informal Learning. In Conference Proceedings. ICERI 2023
  • Mengoni P, Santucci V. Special Issue “Recent Trends in Natural Language Processing and Its Applications”. Applied Sciences. 2023; 13(12):7284 https://doi.org/10.3390/app13127284
  • Biondi, G., Franzoni, V., Li, Y., Milani, A., & Santucci, V. (2023, October). RITA: A Phraseological Dataset of CEFR Assignments and Exams for Italian as a Second Language. In 2023 IEEE International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) (pp. 425-430). IEEE
  • Biondi, G., Franzoni, V., Milani, A., & Santucci, V. (2023, June). Classification of text writing proficiency of L2 learners. In International Conference on Computational Science and Its Applications (pp. 15-28). Cham: Springer Nature Switzerland
  • Sbardella, T., Santucci, V., & Spina, S. (2022). SPOC and flexible language learning with Moodle: the experience at the University for Foreigners of Perugia. In 8th International Conference on Higher Education Advances (HEAd’22) (pp. 1017-1024). Universitat Politècnica de València
  • Milani A, Santucci V, Caraffini F. Special Issue “Emerging Artificial Intelligence (AI) Technologies for Learning”. Applied Sciences. 2022; 12(17):8819 https://doi.org/10.3390/app12178819
  • Spina, S., Fioravanti, I., Forti, L., Santucci, V., Scerra, A., & Zanda, F. (2022). The CELI Corpus: A New Resource for Studying Italian L2 Acquisition. Italiano LinguaDue, 14(1), 116-138
  • Santucci, V., Bartoccini, U., Mengoni, P., & Zanda, F. (2022, July). A Computational Measure for the Semantic Readability of Segmented Texts. In International Conference on Computational Science and Its Applications (pp. 107-119). Cham: Springer International Publishing
  • Biscarini, C., Santucci, V., & Sbardella, T. (2021). Fostering motivation in language learning through technology. In EDULEARN21 Proceedings (pp. 10494-10499). IATED
  • Forti, L., Grego, G., Filippo, S., Santucci, V., & Spina, S. (2020). MALT-IT2: A New Resource to Measure Text Difficulty in light of CEFR levels for Italian L2 learning. In Proceedings of the 12th Language Resources and Evaluation Conference (pp. 7206-7213). The European Language Resources Association (ELRA)
  • Santucci, V., Forti, L., Santarelli, F., Spina, S., & Milani, A. (2020). Learning to classify text complexity for the Italian language using support vector machines. In Computational Science and Its Applications–ICCSA 2020: 20th International Conference, Cagliari, Italy, July 1–4, 2020, Proceedings, Part II 20 (pp. 367-376). Springer International Publishing
  • Sbardella, T., Santucci, V., Biscarini, C., & Nencioni, G. (2020). Can web series improve language learning? A preliminary discussion. CALL for widening participation: short papers from EUROCALL 2020, 309
  • Santucci, V., Santarelli, F., Forti, L., & Spina, S. (2020). Automatic classification of text complexity. Applied Sciences, 10(20), 7285
  • Forti, L., Milani, A., Piersanti, L., Santarelli, F., Santucci, V., & Spina, S. (2019, August). Measuring text complexity for Italian as a second language learning purposes. In Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications (pp. 360-368)

The dissemination of the project “Teaching and Learning Italian as a Second/Foreign Language with AI” includes a crucial phase of sharing the results, methodologies, and innovations developed in the academic context. This initiative aims to reach a diverse audience, involving key stakeholders in the fields of education, linguistics, and Artificial Intelligence. The dissemination strategies are designed to maximize the impact of the project and promote the adoption of innovative practices in teaching Italian as a foreign or second language.