Lookahead Audience Adaptation: Predicting How Alternative Explanations Will Perform for Readers with Different Levels of Expertise

Large language models can generate explanations at different levels of technical complexity, but selecting an appropriate explanation for a particular reader remains challenging. An explanation that is concise and accessible to a novice may omit essential mechanisms for an expert, whereas an explanation optimized for technical completeness may impose unnecessary cognitive demands on a beginner. Conventional audience adaptation commonly relies on explicit instructions, predefined audience categories, user profiles, or post-generation readability evaluation. This paper proposes Lookahead Audience Adaptation (LAA), a conceptual framework for selecting explanation strategies according to their predicted downstream effects on readers with different levels of expertise. Rather than generating a single explanation and evaluating it afterward, LAA constructs multiple candidate explanation plans before full drafting. A lookahead evaluator predicts how each candidate may perform for different reader profiles in terms of comprehension, factual accuracy, conceptual coverage, cognitive effort, perceived usefulness, and explanation cost. A decision mechanism then selects a candidate that maximizes expected audience-conditioned utility while accounting for uncertainty, computational cost, and the risk of oversimplification. The framework represents expertise as a multidimensional and potentially uncertain profile rather than a single proficiency label. It also distinguishes audience adaptation from readability optimization by incorporating prerequisite knowledge, conceptual dependencies, technical precision, and the amount of explanation required to bridge knowledge gaps. A mathematical formulation, candidate-generation procedure, evaluator-training objective, adaptive selection algorithm, and experimental protocol are presented. The proposed method is designed to work with pretrained language models and can support educational explanations, technical documentation, scientific communication, and interactive question answering. No experimental results are claimed. Because existing research has already investigated controllable readability and lookahead-based readability generation, the novelty and effectiveness of LAA must be established through comparisons with these methods and other audience-aware generation approaches. The central research hypothesis is that explicitly predicting audience-conditioned outcomes before drafting can improve the selection of explanations beyond direct audience-conditioned prompting and post-generation reranking under comparable computational budgets.