Learning management systems rely on adaptive algorithms that use learner preferences to personalize the instructional content in form of learning paths. However, these preferences are uncertain in nature, and change over time. The present solutions are either static or purely data-driven missing the dynamic adaption to changes in the preferences and infusion of pedagogical nuances respectively.

This paper introduces an extended variant of Nestor, our Bayesian network engine that models personality traits, learning styles, and learning strategies. This extension overlays a lightweight rule-based mechanism whose “secret recipe’’ lies in the infusion of expert-derived weights adapting learning paths dynamically whenever a learner selects new material in Moodle.

To parameterise these rules, we conducted a structured survey with 12 hand-picked professors and researchers in educational science. Each expert responded to 4 demographic items and 12 item that are distributed across algorithm-overview, scenario-based, and example-based categories, thereby supplying the nuanced weightings that result the personalised recommendations.

This hybrid system (Nestor plus the expert-infused rule layer) operated during the winter term of 2025. 18 students completed an end-of-term questionnaire. Although their learning gains were not recorded, the majority of respondents reported positive or neutral experiences with the dynamically adapted learning paths.

The {Future work} will compare three engines:
(i) the present dynamic, expert-infused rule layer on top of the static Bayesian network,
(ii) purely data-driven machine-learning models that neglect expert weighting, and
(iii) the original static-adaptation Bayesian network without rules.

Analyses of log files, intermediate satisfaction surveys, and pre/post term surveys will clarify whether this on-the-fly adaptation and pedagogical nuance lead to measurable learning benefits.

Keywords: Learning Management Systems, Learning Paths, Bayesian Networks, Rule-Based Systems, Expert Knowledge, Real-Time Adaptation.