Large language models (LLMs) hold significant potential to enhance online learning by automating responses to learner queries and offering personalized, scalable support. However, concerns about bias in LLM-generated responses present challenges to their ethical and equitable use in educational settings. This study explores fairness and explainability in LLM-generated replies within online discussion forums. Specifically, we fine-tuned three state-of-the-art LLMs (GPT-2, Gemma, and LLaMA) using both the original MOOC Posts dataset and a counterfactual version. We then analyzed the sentiment patterns of LLM-generated replies and compared them with human-generated responses. To quantify potential sentiment bias, we introduce absolute distributional sentiment divergence (ADSD) to measure disparities across sensitive attributes, with gender used as a case study. To mitigate bias and enhance transparency, we employed counterfactual fine-tuning by incorporating both factual and counterfactual data, and we used TIGERSCORE, a reference-free explainability metric, to assess response quality. Our findings reveal that LLM-generated responses are generally more neutral than human replies but exhibit varying degrees of sentiment bias across gender. Notably, counterfactual fine-tuning shows promise in reducing this bias, resulting in more balanced sentiment distributions. Additionally, explainability analysis indicates that while newer models (Gemma and LLaMA) outperform GPT-2 in response quality, gaps in accuracy and comprehension remain. This study advances the understanding of bias mitigation and fairness evaluation in LLM-generated educational support, contributing to the development of more equitable, transparent, and responsible AI-driven tools for online learning environments.