## 1 Introduction  
During the COVID-19 pandemic, education systems worldwide transitioned to Emergency Remote Teaching (ERT), transforming in-person instruction into online formats on short notice. This abrupt shift disproportionately impacted students with disabilities, often referred to as students with functional diversity (FD), who encountered barriers in accessing and engaging with remote instruction [1]. According to the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) of the American Psychiatric Association [2], students with FD can be diagnosed with Autism Spectrum Disorder (ASD), dyslexia, Attention Deficit Hyperactivity Disorder (ADHD), physical disabilities, specific learning disorders, or speech and language disorders. With the appropriate support, their needs can be met, and their educational achievements can be promoted, even in educational environments where technology is integrated [3, 4]. Due to their unique physical, cognitive, or sensory needs, students with FD require tailored educational strategies. In inclusive settings, many such students struggled to participate meaningfully when their specific needs were not adequately supported. Early parental reports highlighted negative effects of ERT on learning progress and well-being [5]. To address this gap, the present study applies Natural Language Processing (NLP) and Machine Learning (ML) techniques to analyze Modern Greek interview data collected from parents of students with functional diversity. The use of Modern Greek--a low-resource language in NLP--constitutes a novel contribution of this work, advancing both linguistic and educational research [6]. Parents can provide invaluable insights into the ERT experience for FD students to ensure an inclusive and equitable learning environment. By dividing their views into four topics and employing text analysis techniques, this paper aims to reveal the intricacies and implications of the educational shift to ERT and highlight the unique linguistic aspects of a Modern Greek dataset. Four research questions (RQ) were defined, namely:  
* RQ1: Which challenges and benefits do parents identify regarding ERT for FD students in Greece? * RQ2: How effectively can NLP techniques model the primary concerns of parents regarding ERT? * RQ3: What idiosyncrasies of Modern Greek affect topic identification performance? * RQ4: How efficiently can ML methods extract key information from educational data to guide policy development for the successful adoption of Information and Communication Technology (ICT) in education? This study contributed to educational research by analyzing parental perspectives on ERT for students with FD, identifying key topics in qualitative data, and offering insights into the challenges and effectiveness of ERT. By applying NLP and ML, the study provided a structured approach to understanding parental experiences. The study further emphasized the need for targeted interventions to optimize ERT practices and support students with diverse learning requirements. To the authors' knowledge, this research is the first to perform topic identification in interviews of parents of students with FD regarding ERT. The text corpus was collected and analyzed in this unique context, setting a precedent for future research. The creation of a dataset in Modern Greek enables a more thorough examination of linguistic complexities. The Greek language, with its intricate grammatical and syntactic rules, poses certain challenges for computational analysis. The innovative approach adopted in this research offers a more detailed understanding that conventional methods may overlook. By incorporating preprocessing and data cleaning procedures, the integrity and quality of the dataset were significantly improved. This preparation ensures that the analysis is not only robust but also sensitive to the subtleties within the text corpus.