Sixth Time, Different Story

ADHD, Inclusive Design, GenAI Tools, and the Hidden Infrastructure Behind My Sixth Master’s Attempt

ADHD, Inclusive Design, GenAI Tools, and the Hidden Infrastructure Behind My Sixth Master’s Attempt

I stood at the threshold of the lecture hall, a wavering symphony of hope and resignation playing within me. The cool metal of the door handle felt grounding against my palm, anchoring me as I prepared to embark on my sixth attempt at a Master’s degree. This effort was not a capitulation to past failures, but rather the pursuit of a long-held dream I now dared to see through.

That statement is not intended to dramatise failure, nor to invite sympathy. It is simply a factual description of a long academic journey marked by repeated interruptions, withdrawals, and unrealised potential. Yet beneath these words lies a deep well of frustration. Every withdrawal felt like another lapse in an ongoing struggle, casting shadows over my previous achievements. Over the years, I entered postgraduate programs with intellectual readiness and strong motivation, yet repeatedly found myself unable to sustain the demands of academic work, particularly at the stage where independence, prolonged writing, and self-regulation became unavoidable.

The journey was as much an emotional challenge as it was an academic one, demanding resilience in the face of recurring setbacks.

This Master of Education in Diversity and Inclusion at Trinity College Dublin was the first postgraduate programme I attended after receiving a formal diagnosis of Attention Deficit Hyperactivity Disorder (ADHD). Before, every master's degree attempt felt like a heroic endeavour, requiring relentless effort to sustain focus and momentum, akin to a marathon where each mile was an uphill battle. In contrast, post-diagnosis, the journey was no longer about heroics but about possibility. Understanding my cognitive needs allowed me to transform the process into an achievable pursuit, enabling me to reach milestones with confidence rather than desperation.

The difference was not effort. The difference was not intelligence. The difference lay in three interlocking components that fundamentally altered my relationship with learning: diagnosis, the university system, and the emergence of generative AI tools at a critical moment in higher education.

1. Diagnosis as Epistemic Access to the Self

The most significant change was my ADHD diagnosis.

Before diagnosis, my academic history was framed almost entirely through an institution-level discourse that moralised my academic performance. Inconsistent performance was interpreted as a lack of discipline. Delays were understood as procrastination. Inability to sustain writing was seen as a personal failure of will. Each unfinished degree reinforced the same conclusion: something was wrong with my character.

For instance, during one of my earlier attempts, I vividly remember a meeting with an academic advisor who, rather than offering support or accommodations, sternly emphasised my "need to be more disciplined" and "get my act together."

This not only left me without the necessary support but also compounded my self-doubt. These interpretations arose from systemic norms that equated academic success with continuous productivity and adherence to prescribed timelines, without acknowledging the students’ diverse cognitive profiles. The language of academic assessment often lacked a nuanced understanding of neurodiversity, leading to the mischaracterisation of learning challenges as personal shortcomings.

Diagnosis provided a different kind of knowledge.

It offered an explanatory framework for how my brain works, particularly in relation to executive functions such as task initiation, planning, sequencing, working memory, and sustained attention.

To illustrate, let me take you into one of those moments:

I sat at my desk, a mountain of articles scattered around me, the cursor blinking on a blank document. The task ahead was assembling a literature review, requiring me to organise substantial amounts of information and synthesise them into a coherent narrative; all of which demanded significant planning and working memory. It was like having a browser open with countless tabs, each demanding my attention at once, but never leading to a final destination. As I attempted to categorise sources and draw connections, it felt like trying to complete a puzzle with missing pieces.

Before diagnosis, this process often led to overwhelming stress and halted progress, as my mind struggled to hold various threads of information simultaneously. What had previously appeared as personal shortcomings were revealed as predictable patterns of executive impairment interacting with rigid academic systems.

This understanding was not merely descriptive.

It was strategic.

Knowing how my cognition functions allowed me to anticipate breakdown points and design compensatory strategies around them.

For instance, I utilised a Pomodoro timer in conjunction with a visual checklist to break tasks into manageable intervals, ensuring sustained focus and progress. It shifted my focus from forcing myself into neurotypical modes of productivity to working with, rather than against, my cognitive profile. This shift also brought a profound sense of emotional relief; acknowledging my unique cognitive makeup reduced self-criticism and cultivated self-compassion. The psychological benefit of this acceptance extended beyond productivity, offering a renewed sense of self-worth and understanding.

Diagnosis also enabled access to pharmacological intervention.

The use of stimulant medication did not transform me into a different person, nor did it grant exceptional productivity. What it provided was regulation. It allowed me to reach a functional baseline that many students take for granted. For the first time, I could engage in daily academic tasks without expending disproportionate energy to begin. This regulation manifested through measurable changes: I maintained a consistent daily word count that previously seemed unattainable, and I incorporated scheduled breaks into my routine to manage focus and energy effectively. Previously, I used to struggle with my foggy brain and sluggish feelings that were even hard for me to point out. Several hours, days, and even weeks could pass without any progress, but now I can see progress in my coursework and dissertation daily.

This mattered because postgraduate study does not primarily test intelligence. Instead, it tests regulation, which inherently governs persistence. Recognising this makes the challenges of sustaining effort and managing one's academic demands clearer and more feasible.

2. The University System as an Enabling Environment

The second component was the university system itself.

This programme was situated within an inclusive educational framework informed by Universal Design for Learning. The difference between this environment and my previous experiences was immediately apparent. Barriers that had repeatedly undermined my earlier postgraduate attempts were either reduced or removed altogether.

UDL reframed academic participation away from narrow assumptions about how learning should look. Flexibility in engagement, representation, and expression was not treated as exceptional accommodation but as a baseline design principle. This mattered profoundly for a student whose primary difficulty lies not in understanding complex ideas, but in managing the processes required to produce long, linear academic texts.

Examples of how UDL is implemented include giving all students the option to produce the final assignment for each coursework in any form we find best for communicating our work. Rather than being confined to the traditional 3500-word essay, I benefited from the option to present my work as a recorded slide presentation, submitted as a 15-minute pre-recorded video that maintained academic integrity, in-text citations, etc. The presentation is more visual, allowing my working memory to function better and avoiding cognitive overload.

Equally important was institutional awareness. The language around neurodiversity, disability, and inclusion was not performative. Support structures were visible, accessible, and normalised. Conversations about cognitive difference were no longer confined to medical documentation or individual advocacy, but embedded in teaching practice and assessment design. In contrast, I had previously encountered institutions where the language of inclusion existed primarily as a series of buzzwords in promotional materials and policy documents, with little practical application or impact. In those settings, support was often difficult to access, and the burden of advocacy fell heavily on individual students, perpetuating a cycle of exclusion. This juxtaposition highlights why authentic institutional awareness matters significantly.

Compared to my earlier postgraduate experiences, which were shaped by silent expectations and rigid norms, this environment reduced the hidden curriculum that so often disadvantages neurodivergent students. For the first time, I was not constantly required to translate myself into acceptable academic behaviour.

This did not make the work easy. It made it possible.

3. Generative AI as Cognitive and Technical Infrastructure

The third component was the timing of this degree in relation to the emergence of generative AI tools.

It is difficult to overstate how significant this was.

For students whose primary challenges lie in executive functioning rather than conceptual understanding, academic work is often derailed not by ideas but by process. Planning, organising, structuring, formatting, and managing cognitive load consume resources that should be available for thinking.

Generative AI did not replace intellectual labour. It supported it.

Throughout the dissertation process, AI tools functioned as an external cognitive scaffold. They helped me organise tasks, maintain coherence across chapters, and manage technical demands that would otherwise have consumed disproportionate energy. Importantly, these tools did not make decisions for me. They enabled me to make more sustainable decisions.

At this point, transparency matters.

How I Used AI Tools During the Dissertation

The use of AI and advanced digital tools during my dissertation was deliberate, bounded, and reflective. Each tool served a specific function aligned with its intended design.

ChatGPT Plus

I used ChatGPT Plus as an advanced generative AI assistant to support aspects of my writing and research planning. According to the official platform description, ChatGPT Plus provides access to enhanced reasoning and expanded capabilities, including deeper research workflows and improved task management suited to complex work such as writing and structuring a dissertation.

My use of ChatGPT Plus included:

• assisting with academic writing queries and overall structural planning

• providing formative feedback on draft sections I had written and highlighting overlapping or repetitive points

• suggesting reorganisation of sections to improve coherence and logical flow

• explaining and troubleshooting technical issues, particularly related to Microsoft Word formatting and document consistency

These uses align with the intended functions of ChatGPT Plus in supporting complex reasoning, extended context handling, and intelligent assistance for academic writing and project planning, while maintaining complete authorial control over the intellectual content of my work.

Elicit.com

I used Elicit as an AI-powered tool for literature discovery and organisation. According to its official description, Elicit is designed to support researchers in finding, summarising, and extracting information from academic literature, and includes workflows for systematic reviews, research reports, and semantic search across a large corpus of research papers.

My use of Elicit included:

• building and managing a personalised bank of research papers and linked notes

• using Elicit’s semantic search features to identify relevant and related literature

• organising research material to support the development of my literature review

• synchronising references and metadata with EndNote for citation management

These uses align with Elicit’s intended function of accelerating literature discovery, organisation, and synthesis within academic research workflows, while leaving interpretive and analytical decisions to the researcher.

Grammarly Pro

I used Grammarly Pro to refine written sections of my dissertation. According to official information, Grammarly’s AI-powered tools support grammar and spelling checks, tone adjustment, and broader writing suggestions, offering assistance as users compose text across different writing environments.

My use of Grammarly Pro included:

• checking spelling and grammatical accuracy

• improving academic tone and clarity

• adjusting phrasing and sentence structure where appropriate

• supporting coherent and audience-appropriate academic writing

These uses reflect Grammarly’s intended role in supporting clear, professional communication through AI-assisted writing tools, without shaping the substantive content or analytical direction of the work.

Scholarcy

I used Scholarcy to support reading and initial comprehension of research articles. According to official information, Scholarcy converts academic documents into interactive summaries and flashcards, enabling users to identify key points and organise research insights rapidly.

My use of Scholarcy included:

• generating structured summaries of academic papers to support initial orientation

• visually highlighting essential concepts, methods, and findings

• organising insights to assist with the review and synthesis of the literature

These uses align with Scholarcy’s intended function of simplifying complex academic texts and supporting efficient organisation of research knowledge, while full engagement with sources remained essential to my work.

LiquidText Pro

I used LiquidText Pro as a central tool during the post-data collection phase to support close reading, sensemaking, and early analytical engagement with interview transcripts. This stage was particularly critical, as it marked the transition from data collection to qualitative analysis. LiquidText Pro enabled me to work directly with the data in a way that supported sustained attention and cognitive engagement.

My use of LiquidText Pro included:

• marking and annotating significant segments of interview transcripts

• visually linking related excerpts within and across different interviews

• connecting emerging concepts and patterns as part of an iterative reading process

• externalising early analytical thinking through spatial organisation of excerpts and notes

Using LiquidText Pro on an iPad with the Apple Pencil was especially important. This setup enabled tactile, handwritten interaction with the data, such as freehand annotation, circling, drawing connections, and rearranging ideas spatially. For me, this mode of engagement encouraged deeper cognitive processing than linear screen-based reading and reduced the mental load of navigating lengthy transcripts.

LiquidText Pro served as a visual and organisational workspace rather than an analytical tool. While it facilitated data exploration and organisation, all qualitative analysis, including theme development, interpretation, and integration with theoretical frameworks, was carried out manually by me, following the specified qualitative methodology.

WhisperTranscribe

During data preparation, I used WhisperTranscribe to convert interview video recordings into text. The platform employs Whisper-based automatic speech recognition models that are designed to produce highly accurate transcripts, even for complex audio and lengthy recordings.

WhisperTranscribe was the first tool that genuinely boosted my confidence in realising I could complete this dissertation. During my earlier postgraduate attempts, transcription alone often took weeks, sometimes months, becoming a significant source of delay and discouragement. The mental and time-consuming aspects of manual transcription repeatedly slowed progress before analysis could even start.

In contrast, WhisperTranscribe enabled interview recordings to be transcribed in less than ten minutes per interview. This dramatic reduction in time fundamentally changed the rhythm of the research process. Instead of transcription acting as a bottleneck, it became an manageable and predictable step that allowed me to move swiftly from data collection to analysis.

My use of WhisperTranscribe included:

• transcribing interview recordings into text files for qualitative analysis,

• generating editable transcripts that could be corrected and annotated,

• reading the transcript while simultaneously listening to the original audio recording to verify accuracy and completeness.

The ability to listen to the interview while reading the transcript was crucial. It allowed me to check nuance, tone, and emphasis, ensuring that the transcripts faithfully represented participants’ voices. All transcripts were reviewed and corrected manually before analysis.

These uses align with WhisperTranscribe’s primary function as an automated speech recognition system that accurately converts audio and video into text. More importantly, in my case, the tool served as a vital enabler. It eliminated a long-standing procedural barrier and provided the first tangible evidence, at a practical level, that this time, writing and completing a thesis was genuinely achievable.

Why This Time Was Different

This Master’s degree was not completed because I finally learned to try harder.

It was completed because the conditions for learning changed.

Diagnosis gave me epistemic access to myself. The university system reduced structural barriers that had previously made postgraduate work untenable. Generative AI provided cognitive and technical infrastructure that allowed me to manage executive demands without constant self-depletion.

Together, these three components transformed postgraduate study from an endurance test into a sustainable intellectual practice.

For neurodivergent students, success in higher education is rarely about potential. It is about fit. When understanding, systems, and tools align, what once required extraordinary effort becomes ordinary participation.

This was my sixth attempt.

It was also the first time the system met me halfway. How will educational institutions meet tomorrow's neurodivergent scholars halfway? This question invites educational leaders and policymakers to reflect on the evolving needs and strengths of neurodivergent learners. By actively seeking to understand and integrate inclusive practices, institutions can transform higher education into a truly equitable and empowering environment for all students.