An Intelligent Software Agent for Personalized Tutoring

Software agents play a central role in modern artificial intelligence, particularly in digital environments that require autonomy, adaptability, and goal-oriented behaviour. Among their many applications, intelligent tutoring systems stand out as a promising domain in which software agents can deliver personalised, context-aware support to learners. These systems aim to adapt content, strategies, and feedback to individual students’ needs, thereby enhancing learning outcomes and engagement. This dissertation will focus on the development of an intelligent software agent designed to act as a tutor, supporting the creation and dynamic management of personalised study plans.

The primary objective of the dissertation is to design, implement, and evaluate a goal-driven tutoring agent that operates within a virtual learning environment. The agent will monitor a learner’s progress, propose tailored study plans, and adapt its guidance based on performance, preferences, and evolving goals. The project will draw on models of autonomous software agents, particularly utility-based and plan-based approaches, and will use the Letta platform to support the design, execution, and coordination of agentic behaviours.

The dissertation will pursue the following goals

- Survey the literature on intelligent tutoring systems and software agents, with a focus on approaches that support autonomy, personalisation, and decision-making under uncertainty.

- Design an agent architecture that can represent user goals, monitor learning activities, and select pedagogical strategies accordingly. The agent should be capable of interfacing with structured curricular resources and managing evolving study plans.

- Implement the tutoring agent using a platform, such as Letta, Jason or other existing alternatives, which offer a structured framework for agent specification, including goal definition, capability management, and reasoning over action choices.

- Enable multi-modal interaction within a software environment, such as recommending resources, issuing reminders, or querying user preferences and goals. While the system will remain fully digital, it may simulate dialogue-like exchanges with the learner to enhance responsiveness.

- Evaluate the effectiveness of the tutor agent, both in terms of its internal reasoning capabilities and its ability to adapt to different learner profiles. Metrics may include goal completion rate, alignment with user preferences, and perceived usefulness in simulated user trials.

The dissertation is expected to lead to a functioning prototype and to include an analysis of the challenges and trade-offs involved in developing software agents for personalised education. Potential extensions include integrating domain-specific knowledge models, supporting long-term learning trajectories, or incorporating multiple agents for collaborative learning support.