Large Language Models (LLMs) are increasingly influencing software engineering practice and education, yet little research has examined their systematic integration using stakeholder evidence and quantitative decision-support models. This study presents an exploratory familiarity-allocation framework for analyzing motivating and demotivating factors in LLM integration in software engineering education. Building on our earlier literature-based taxonomy, we evaluated 19 factors (nine motivators and ten demotivators) through a survey of 126 stakeholders from multiple countries. Naive Bayes and Logistic Regression were used to examine associations between factor ratings and high self-reported LLM familiarity. Both models reached an accuracy of 0.461, below the majority-class baseline, so their probabilities were used as exploratory relative scoring functions rather than individual-level predictions. These estimates were fed into a Genetic Algorithm with a defined allocation score to support Global Factor Allocation (GC), Local Factor Allocation (LC), and category-level Delta-Fitness analyses. The most highly rated motivators were Programming Assistance and Debugging Support, Personalized and Adaptive Learning, and AI as a Learning Partner. The most highly rated demotivators were Plagiarism and Intellectual Property Concerns, Over-Reliance on AI in Learning, and Reduced Critical Thinking and Problem-Solving. Under the fitted models and allocation assumptions, GC assigned higher levels to integrity- and governance-related factors, while LC revealed further pedagogical and risk-related patterns within categories. These configurations are exploratory scenarios, not validated implementation priorities or evidence of educational effectiveness, and classroom and institutional validation is needed before the framework can guide practice.

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