The exceptional progress in artificial intelligence is transforming the landscape of technical jobs and the educational requirements needed for these. This study’s purpose is to present and evaluate an intuitive open-source framework that transforms existing courses into interactive, AI-enhanced learning environments. Our team performed a study on the proposed method’s advantages in a pilot population of teachers and students which assessed it as “involving, trustworthy and easy to use”. Furthermore, we evaluated the AI components on standard large language model (LLM) benchmarks. This free, open-source, AI-enhanced educational platform can be used to improve the learning experience in all existing secondary and higher education institutions, with the potential of reaching the majority of the world’s students.

The exceptional progress in artificial intelligence is transforming the landscape of technical jobs and the educational requirements needed for these. This study’s purpose is to present and evaluate an intuitive open-source framework that transforms existing courses into interactive, AI-enhanced learning environments. Our team performed a study on the proposed method' advantages on a pilot population of teachers and students which assessed it as "involving, trustworthy and easy to use". Furthermore, we evaluate the AI components on standard large language models (LLM) benchmarks. This free, open-source, AI-enhanced educational platform can be used to improve the learning experience in all existing secondary and higher education institutions, with the potential of reaching the majority of world’s students.

With the growing use of machine learning models for supporting financial decisions, financial institutions face increasing pressure to provide explanations, traceability and stable risk assessments of their decision-making processes. Current approaches to requirements gathering do not take into account the realities of this domain since they are based on the assumption that decision requirements can be formulated at an early stage and then be refined incrementally. In reality, however, financial decision objectives; data availability; analytical feasibility; and process governance constraints evolve simultaneously and interactively and are often discovered only by way of exploratory analyses or preliminary validation activities. This paper introduces PRIME-RE, a parallel and recursive methodology for eliciting requirements for machine-learning based financial decision support systems. PRIME-RE structures the requirements gathering and elicitation process in four concurrent streams: decision objectives; data feasibility; analytical feasibility; and process-governance constraints, including regulatory constraints. When conflicts arise between streams, the methodology provides for controlled recursive refinement of those streams to resolve the conflicts. The results of the methodology have been validated using two sources: an expert survey with 25 practitioners and prior empirical research on how to structure unstructured data for evaluating risk. In summary, a conceptual architecture is introduced illustrating how non-functional requirements (e.g. stability and interpretability) influence system design and how unstructured information can be integrated as contextual signals for the purpose of informing risk evaluations while preserving core risk assessment capabilities. The results show that parallel and recursive requirements elicitation enables the development of credible, transparent and compliant ML systems.