Giorgia Adorni

Ph.D. Student
Intelligent Tutoring and Assessment Systems in Education

About Me
I am pursuing a PhD at IDSIA USI-SUPSI (Dalle Molle Institute for Artificial Intelligence), where I focus on developing Intelligent Tutoring and Assessment Systems to evaluate students’ development of computational thinking skills. My work combines educational technologies and AI to create innovative learning tools.From Art to Technology
My academic journey didn’t start in computer science. I attended an arts high school with a focus on architecture, where creativity and artistic expression were at the forefront. During this time, I also explored other forms of art, such as photography and even tattoo artistry. However, despite my passion for the arts, I found myself increasingly drawn to technology. I was fascinated by how technological advancements can shape the world and improve people's lives, so I took a bold step. I enrolled in Computer Science at the University of Milano-Bicocca (UNIMIB), Italy.
After completing my B.Sc. in Computer Science in 2018, I pursued a Double Master's Degree in Informatics between UNIMIB and Università della Svizzera italiana (USI) in Switzerland, graduating in 2020. This international experience broadened my horizons, giving me a unique perspective on how technology can be applied to different contexts and cultures.My Ph.D. Research
My Ph.D. research is focused on Intelligent Tutoring and Assessment Systems (ITAS) aimed at improving computational thinking in students from different educational levels. My work spans diverse regions of Switzerland, where I conduct exploratory studies to explore how AI can assess and enhance the development of algorithmic thinking skills in young learners across different linguistic and educational contexts. Through my research, I aim to contribute to the future of education, creating adaptive learning environments that tailor instruction to individual needs, enhancing both engagement and learning outcomes.
I'm particularly passionate about how AI and robotics can be used in educational settings, bridging the gap between theoretical knowledge and practical application. I believe these technologies have the potential to revolutionise how we approach education, offering personalised learning experiences that adapt to each student's pace and style.Academic Interests
My academic interests include: Intelligent Tutoring Systems, AI for Autonomous Robotics, Educational Robotics, Human-Robot Interaction, Computational Thinking, Learning Technologies and Machine Learning.Future Goals
In the future, I aim to deepen my research into how AI-driven tools can democratise education by making high-quality, personalised learning accessible to all students. I’m particularly interested in expanding my work to include interdisciplinary collaboration, integrating insights from cognitive science, pedagogy, and artificial intelligence to build more holistic educational technologies.Outside of Academia
Outside of my academic pursuits, I am driven by a desire to make the world a better place. I believe that small acts of kindness can have a big impact, whether it's mentoring a student, lending a hand to a colleague, or simply being there for someone in need.
Music plays a big role in my life as well—it’s my greatest passion outside of research. Singing and listening to music help me stay balanced, keeping my creative spirit alive while navigating the challenges of academia.Get in touch!

Projects

2021–2022

gansformer-reproducibility-challenge
Replication of the novel Generative Adversarial Transformer.
(Advanced Topics in Machine Learning course @USI)

2019–2020

lstm-for-text-prediction
Implementation of a text generator based on Long Short-Term Memory (LSTM).
(Deep Learning Lab course @USI)

deep-q-network-atari-games
Implementation of a Deep Q-Network agent, based on Mnih et al., 2015.
(Deep Learning Lab course @USI)

ros-turtlesim-controller
A simple ROS node that controls a turtle in turtlesim.
(Robotics course @USI)

mighty-controller
An open loop controller that moves a Thymio in Gazebo.
(Robotics course @USI)

learning-relative-interactions-through-imitation
Learn to perform specific interactions between a marXbot robot and objects in the environment, using the simulator Enki.
(Robotics course @USI)

learning-robot-swarm-controllers
Master’s thesis project that simulates robot swarms for learning communication-aware coordination using imitation learning approaches.
(@USI)

2018–2019

mosquito-disease-spread-rate-prediction
A supervised classification model capable of predicting whether or not WNV virus will be detected in a certain trap every week.
(Data Technology and Machine Learning course @UNIMIB)

viterbi-algorithm-for-hmm
An implementation of the Viterbi Algorithm for training Hidden Markov Models.
(Probabilistic Models for Decisions course @UNIMIB)

likelihood-weighting-sampling
A Python implementation of the likelihood weighting approach for Bayesian Network sampling.
(Probabilistic Models for Decisions course @UNIMIB)

misspelling-corrector
Misspelling corrector based on Hidden Markov Models and Noisy Channel Model.
(Probabilistic Models for Decisions course @UNIMIB)

dct2-compression
DCT2 implementation with Faster-than-light Fourier Transform.
(Methods of Scientific Calculation course @UNIMIB)

comparison-of-cholesky-decomposition-implementations
A study of the implementation of the Choleski method for the resolution of linear systems for sparse, symmetric and positive definite matrices. Comparison based on different open source programming environments and MATLAB implementation.
(Methods of Scientific Calculation course @UNIMIB)

human-disease-network
Human Disease Network: clustering and performance evaluation.
(Data Analytics course @UNIMIB)

2017–2018

neigh
Neighbour Joining algorithm for the creation of Phylogenetic Trees.
(Bioinformatics course @UNIMIB)

chess-recognition
A MATLAB library for recognition of La Settimana Enigmistica chess games.
(Image Processing course @UNIMIB)

learning-personality
Bachelor internship project that uses Neural Networks to predict personality traits from text written in natural language.
(@UNIMIB)

Thesis

Master thesis:
Simulation of robot swarms for learning communication-aware coordination
GitHub code

Bachelor thesis:
Neural networks for learning personality traits from natural language
GitHub code

Research

Recent publications

G. Adorni*, I. Artico, A. Piatti, E. Lutz, L. M. Gambardella, L. Negrini, F. Mondada, D. Assaf (2024). Development of algorithmic thinking skills in K-12 education: A comparative study of unplugged and digital assessment instruments. Computers in Human Behavior Reports, vol 15, 100466.G. Adorni*, S. Piatti, V. Karpenko (2024). Virtual CAT: A multi-interface educational platform for algorithmic thinking assessment. SoftwareX, vol 27, 101737.G. Adorni*, A. Piatti. (2024). The virtual CAT: A tool for algorithmic thinking assessment in Swiss compulsory education. arXiv preprint arXiv:2408.01263.G. Adorni*, A. Piatti, E. Bumbacher, L. Negrini, F. Mondada, D. Assaf, F. Mangili, L. M. Gambardella. (2024). A theoretical framework for the design and analysis of computational thinking problems in education. arXiv preprint arXiv:2403.19475.S. Corecco, G. Adorni*, L. M. Gambardella. (2023). Proximal Policy Optimization-Based Reinforcement Learning and Hybrid Approaches to Explore the Cross Array Task Optimal Solution. Machine Learning and Knowledge Extraction, vol. 5, no. 4, pp. 1660-1679.G. Adorni, F. Mangili, A. Piatti, C. Bonesana, A. Antonucci. (2023). Rubric-based Learner Modelling via Noisy Gates Bayesian Networks for Computational Thinking Skills Assessment. Journal of Communications Software and Systems, vol. 19, no. 1, pp. 52-64.F. Mangili, G. Adorni, A. Piatti, C. Bonesana, A. Antonucci. (2022). Modelling Assessment Rubrics through Bayesian Networks: a Pragmatic Approach. International Conference on Software, Telecommunications and Computer Networks (SoftCOM), pp. 1-6.A. Antonucci, F. Mangili, C. Bonesana, G. Adorni. (2022). Intelligent Tutoring Systems by Bayesian Nets with Noisy Gates. The International FLAIRS Conference Proceedings, vol. 35.A. Piatti, G. Adorni*, L. El-Hamamsy, L. Negrini, D. Assaf, L. M. Gambardella, F. Mondada. (2022). The CT-cube: A framework for the design and the assessment of computational thinking activities. Computers in Human Behavior Reports, vol 5, 100166.A. Antonucci, F. Mangili, C. Bonesana, G. Adorni. (2021). A New Score for Adaptive Tests in Bayesian and Credal Networks. European Conference on Symbolic and Quantitative Approaches with Uncertainty. Springer, Cham. pp. 399-412.* corresponding author