## 6 Conclusions  
The dataset serves a dual purpose: (i) it fills a significant gap in the literature by providing a targeted corpus within the specialized context of ERT, and (ii) it constitutes a valuable resource for the Modern Greek linguistic infrastructure. It is unique in its focus on four critical dimensions: (i) Material and Technical Conditions, (ii) Educational Dimension, (iii) Psychological/Emotional Dimension, and (iv) Learning Difficulties and ERT (RQ1). Integrating multiple preprocessing techniques enhances model performance, confirming that thorough text normalization improves ML model quality (RQ3). By dividing interviews into sentences and categorizing them into the four topics, the study contributes a structured resource for Modern Greek topic identification (RQ3). Written in Greek, the dataset contains notable linguistic complexities and includes verbal-oral expressions, enhancing its richness and analytical depth. The intricate structures and diverse vocabulary serve as a strong foundation for evaluating different preprocessing and ML techniques. Despite lower performance indicators, the dataset's novelty remains crucial. The comprehensive application of preprocessing techniques strengthens its value. In such contexts, lower metrics may offer unique insights into specific topics rather than being considered drawbacks. The current model's performance results should not be interpreted as ideal or conclusive. This study is intended as a benchmark investigation based on a small, purposefully selected sample of parents of children with functional diversity. The primary objective was to explore their unique perspectives within a specific educational context during ERT. Given the novelty of both the dataset and the applied methodology, this work serves as an initial step in this research direction, offering early insights and a foundation for future studies. While the present study does not adopt a longitudinal design nor incorporate a multi-stakeholder analysis, these elements are planned as part of a broader research project. Future work will focus on expanding the dataset to include a more diverse set of participants and educational settings. Additionally, the adoption of more advanced learning architectures will be explored to enhance classification performance across topic categories. To our knowledge, no prior studies have applied this methodology to such data in the Greek context. As such, this exploratory study aims to lay the groundwork for more robust and generalizable analyses in subsequent phases. Continued refinement, broader data collection, and methodological improvements are expected to substantially enhance the reliability and applicability of the findings. Recurring themes in parental interviews revealed challenges faced by students with FD, particularly in digital learning strategies, communication, and educational and psychological aspects of remote learning. This study demonstrated how data-driven approachescould analyze accessibility barriers, improve lesson structure, and refine the teaching experience. These insights could support policymakers in developing evidence-based ICT strategies that meet diverse learner needs. ML provides a structured approach to processing qualitative data, enhancing the understanding of stakeholder perspectives. NLP and ML contributed to optimizing instructional approaches by ensuring ICT tools were adapted to diverse student needs. The extracted information could inform policies for more inclusive and effective digital learning environments (RQ4), as ML methods can transform qualitative insights into actionable knowledge. Despite the dataset's small size, the initial success in classification shows promise for NLP methodologies in educational and psychological domains that are traditionally underexplored. The dataset enriches the existing corpus and highlights the need for interdisciplinary research involving NLP.