B8302 - INTELLIGENZA ARTIFICIALE E MODELLI FORMATIVI

Learning outcomes

At the end of the course, the student: - knows the fundamental principles of Artificial Intelligence (AI) and their implications in the educational context, with particular reference to educational models; - knows technological tools and AI-based platforms that can be used in educational settings; - knows the ethical and social implications related to the use of AI in education and training; - knows theoretical models that support the integration of AI in teaching mediation processes and lifelong learning; knows the characteristics of different educational contexts and how AI can be used to address specific issues or improve training processes; is able to critically analyze and assess the potential and limitations of AI in enhancing the effectiveness of educational models; is able to promote the informed use of AI among educational professionals, fostering a critical and responsible approach; is able to identify opportunities and challenges related to the use of AI in education, through participatory design strategies and adaptation to the needs of diverse users; is able to reflect on the skills related to the use of AI in educational models; is able to develop lifelong learning projects that integrate AI to support continuous education and professional development.

Course contents

The course is divided into two modules, covering the following topics:

• Identification of the fundamental principles of artificial intelligence and their implications in educational contexts.
• Analysis of educational models that support the integration of AI into teaching mediation processes and lifelong learning.
• Knowledge of the main AI-based tools and platforms applicable in innovative training settings.
• Acquisition of AI literacy skills for a conscious and responsible use of artificial intelligence in educational contexts.
  
• Analysis of continuous professional development projects that integrate AI to support lifelong learning and professional upskilling.
• Development of autonomous reflections to identify both the potential and the critical issues related to the use of AI, in relation to the characteristics and needs of different educational contexts.

Readings/Bibliography

Required texts for both modules, for all attending and non-attending students:

• Chiara Panciroli, Pier Cesare Rivoltella, Pedagogia algoritmica. Per una riflessione educativa sull'intelligenza artificiale. Morcelliana-Scholé, 2023.
• Chiara Panciroli, Pier Cesare Rivoltella (a cura di), Didattica delle New Literacies, Mondadori, 2025.

The course programme also applies to Erasmus students. Erasmus students are invited to contact the teachers to discuss, where appropriate, any alternative arrangements for the examination.

Teaching methods

The course adopts the blended learning approach established by the Degree Program, integrating in-person and online activities to foster active participation and provide ongoing support for the acquisition of knowledge, abilities, and skills. The learning pathway combines lectures and activities carried out in the classroom with guided asynchronous online activities, accompanied by formative feedback from the instructor, along with synchronous remote discussion and support sessions.

This teaching organization aims to respond more effectively to students' needs, promoting continuous and meaningful interaction throughout the entire learning pathway. Indeed, the blended model supports access to learning, encourages active participation, and offers opportunities for remediation, consolidation, and reinforcement of acquired skills. For detailed information on the organization of teaching activities and modes of participation, please consult the course page on the Virtuale platform, as well as the lecture schedule.

Assessment methods

The teaching activity concludes with an exam graded on a 30-point scale. The exam is unified for both modules.

Written exam. The exam consists of two parts:

Part I: An individual examination, completed without the use of Artificial Intelligence tools, consisting of both open-ended and closed-ended questions. This part is designed to assess students' knowledge, understanding of the course content, and ability to apply the concepts covered during the course.

Part II: The production of a written assignment with the support of Artificial Intelligence tools, used consciously and critically as cognitive support. This part also requires a metacognitive reflection on the interaction with AI, aimed at analysing the strategies adopted, critically evaluating the generated outputs, and discussing both the added value and the limitations of AI's contribution to the learning process and the development of the written assignment.

E-tivities are a compulsory component of the course and contribute to the final assessment of the course according to a weighting established in advance by the instructor and communicated to students during the course (and published on Virtuale). Each e-tivity is assigned a maximum score established by the instructor and communicated to students. The overall score obtained in the e-tivities contributes to the final exam grade, expressed on a 30-point scale, together with the assessment of the final exam.

If a student does not complete or submit the required activities within the deadlines agreed with the instructor (for example, students taking the exam in an academic year subsequent to the one in which the course was offered, or students who do not meet the established deadlines), and in any case by the final exam session of the academic year, additional study requirements may be assigned. These additional requirements, as well as the exam format, will be agreed upon with the instructor and may include the study of additional texts, the consultation of further learning resources, or the completion of question-based assessments and/or further in-depth activities.

Students with DSA or disabilities: it is recommended that they contact the responsible University office (https://site.unibo.it/studenti-con-disabilita-e-dsa/en/for-students) as soon as possible. The request for adaptation must be submitted in advance (15 days before the exam date) to the lecturer, who will assess the appropriateness of the adjustments, considering the teaching objectives.

Teaching tools

• Digital resources: videos, podcasts, handouts, articles, case studies, and slides.
• Digital platforms (Virtuale, Teams, MOdE).
• Seminars with experts.

For students with special needs and students coming from other cultures, please contact the teacher.

 

Office hours

See the website of Chiara Panciroli

See the website of Salvatore Messina