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Mary Tedeschi, professor at ÌÇÐÄvlog¶ÌÊÓƵÌÇÐÄvlog¶ÌÊÓƵ™s Seidenberg School of Computer Science and Information Systems, spent much of 2025 doing what she does best: teaching her students and guiding them through research to such a level that they presented their papers at two prestigious international virtual education conferences: based in Valencia, Spain and in Palma, Spain.

Working alongside both undergraduate and graduate Pace students, and in collaboration with peers from and , Professor Tedeschi's research bridged technical innovation, pedagogical advancement, and human-centered design. These projects, which explore everything from artificial intelligence in student advising to foundational programming patterns, demonstrate the real-world impact of student-faculty research partnerships that is so central to the Pace experience.

INTED 2025: From Code to Classroom Impact

At INTED (International Technology, Education and Development Conference) 2025, an annual conference spotlighting innovation and technology in education, Professor Tedeschi and her team of students from Pace and City Tech CUNY presented two projects.

The first paper, Teaching the Iterator Pattern in Introductory Programming Courses, tackled the issue of introductory programming courses often struggling to bridge theory and practice. In response, this study focused on introducing the iterator pattern (a behavioral design pattern) early in the curriculum to build students' foundational programming skills in languages like Python and Java.

The research leveraged a combination of visual tools, hands-on-practice, pair programming, alternative teaching approaches, and peer collaboration, all reinforced through assessments and reflective writing. Results showed improvement in student comprehension, with the students expressing appreciation for the hands-on approach and finding it a more engaging learning environment.

The project also gave graduate students a unique opportunity to deepen their own understanding by contributing to the curriculum used to teach undergraduates at different institutions, further bridging theory and practice for the researching students as well.

For their second INTED presentation, AI-Powered Customer Support for Academic Counseling and ÌÇÐÄvlog¶ÌÊÓƵ Guidance, Professor Tedeschi and her students explored how AI tools are reshaping academic and career advising. The study assessed how systems like IBM Watson Education and AI-powered chatbots can provide scalable, personalized, ethical, and around-the-clock guidance to students, which would be especially helpful in large educational institutions where counselor-to-student ratios are imbalanced (they can be as high as 1 counselor per 500 students in some cases).

Using natural language processing (NLP) and predictive analytics, these tools can help students track progress, receive course recommendations, and prepare for internships and careers. However, the research also outlined key ethical challenges: mitigating algorithmic bias, ensuring data privacy, and balancing AI efficiency with human empathy.

The team concluded that hybrid models in which human advisors use AI to their benefit may be the most ethical and effective path forward.

EDULEARN 2025: Simplifying Data, Amplifying Access

At EDULEARN (International Conference on Education and New Learning Technologies) 2025, Professor Tedeschi and her students unveiled a technical, future-facing project: the development of an AI-driven database system designed to make data management accessible to non-technical users.

The paper, An Advanced AI-Driven Database System, outlined a platform that uses large language models (LLMs) like GPT-4 and reinforcement learning to automate core database functions, ranging from schema generation to natural language querying and backend optimization.

The team, a collaboration of students from both Pace and NYU, built a modular architecture that supports multiple database types (e.g., SQL, NoSQL, vector, graph) and employs AI to automatically detect data types, translate user queries, and adjust performance. In testing, the system responded to user questions in plain language, improved speed and accuracy, and adapted automatically to changing needs. The project illustrates how AI can simplify complex data tasks while also raising important conversations about ethics, particularly around accuracy, transparency, and data privacy.

Collaboration and Global Exposure

From building smarter database tools to reimagining student support systems, the work of Professor Tedeschi and her team highlights how deeply involved Pace students are at every stage of their research projects. With faculty mentorship and collaboration across New York City institutions, our students gain real-world experience as they shape research, guide implementation, and present their findings on the global stage.