Cyberpsychology Dissertation Titles
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Cyberpsychology Dissertation Titles

Info: 1557 words(1 pages) Cyberpsychology Dissertation Titles
Published: 04th December 2025 in Cyberpsychology Dissertation Titles

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Introduction

The field of cyberpsychology looks at how the rapid changes in the digital landscape impact our psychological experiences. The rapid development of artificial intelligence, virtual reality and emerging technologies creates a diverse and fully-fledged set of psychological and ethical issues around concepts such as trust, bias, accountability, identity change, and how personalised digital environments affect our ability to understand each other. Through this work, the suggested dissertation titles demonstrate the gaps in our understanding of human behaviour and thinking, and help create new opportunities for interdisciplinary research connecting the psychology of humans with the latest digital technology.

Cyberpsychology Dissertation Titles | phdassistance.com

1. Closing the Technology–Therapy Gap: Advancing Evidence-Based Protocols for VR-Supported Identity Reconstruction

The virtual reality (VR) gadgets are currently changing very fast, but their usage in health care—mainly in psychotherapy—remains very low. The medical field does not have any formal plans or certified methods that would allow it to use VR for therapy, even though the technology is capable of facilitating profound psychological change. This situation raises doubts for the therapists and also makes it difficult for the adoption of systems like EYME, which is specifically developed for aiding in the understanding and reconstruction of one’s identity. The authors Garcia-Gutierrez, Montesano, and Feixas (2025) in the Journal of Clinical Psychology point out this disparity and ask for the development of strong protocols and evaluation frameworks that are to remain for a long time, as the only way to make therapy with the help of VR both clinically valid and widely accepted.

Problem Statement:
There are no standardised, evidence-based protocols in clinical practice that take into account the use of cyberpsychology VR therapy. Thus, the question of the extent to which VR-assisted identity reconstruction can lead to long-term effects is still unanswered, and consequently, clinicians are not provided with the right support for safe, consistent, and effective practices.

Research Gap:

Despite the fact that recent research shows the potential of VR for identity exploration, there is no literature that provides a comprehensive framework that combines all the above procedures, mechanisms of psychological change, guidelines for the use of specific platforms, and measures for long-term outcomes. There is no comprehensive model that could successfully guide the professionals in applying VR for identity-related therapy, ensuring, at the same time, clinical validity, effectiveness, and patient safety.

Research Question:
Which clinical protocols and support systems are necessary for the seamless integration of VR into identity-related psychotherapy, and in what ways can such interventions lead to stable and lasting identity transformation?

Outcome:
The thesis will create a compilation of evidence-based clinical protocols for VR-assisted identity therapy, which will comprise platform-specific guidelines (e.g., EYME), therapist implementation procedures, and a long-term assessment model for monitoring psychological change. This framework will connect the rapidly evolving virtual reality mental health research with its safe and effective integration into clinical practice.

Reference:

Garcia-Gutierrez, A., Montesano, A., & Feixas, G. (2025). Using Virtual Reality to Promote Self-Identity Reconstruction as the Main Focus of Therapy. Journal of Clinical Psychology, 81, 345–354.   https://doi.org/10.1002/jclp.23771

2. Fragmented Knowledge: Investigating Epistemic Fragmentation as a Structural Source of Hermeneutical Injustice in Algorithmic Profiling Systems

Digital environments that are algorithmically curated more and more personalise the online experiences of individuals and thus narrow the overlap of shared informational worlds. The process of isolating certain kinds of knowledge from the rest, referred to as epistemic fragmentation, hampers the construction of the necessary interpretative resources for the communities to be able to identify and contest the digital harms that have been around for some time. The article by Milano and Prunkl (2025) in Philosophical Studies brings this problem to light and illustrates how algorithmic profiling can systematically erode the collective meaning-making structures, while at the same time, there are no comprehensive analytical models in the current scholarship to explain this mechanism.

Problem Statement:
The present body of research falls short in providing a clear picture of how the personalisation of online environments through algorithms leads to the dissipation of common hermeneutical resources, thus leaving people unable to recognise, articulate, or even challenge the algorithmic harms.

Research gap:

Current research has been talking about algorithmic personalisation and filter bubbles, but these processes have not been explained in a single model as causing fragmentation of shared knowledge and, thus, weakening of collective meaning-making. The academic world has no common approach that could connect epistemic fragmentation with hermeneutical injustice, thus leaving the structural mechanisms behind reduced community understanding mainly unstudied.

Research Question:
In what way does the epistemic fragmentation caused by algorithmic profiling psychology research contribute to the hermeneutical injustice of the individuals’ ability to share, compare, and collectively understand their experiences being weakened?

Outcome:
A conceptual–analytical model that illustrates how the digital environments being fragmented lead to the emergence of epistemic injustice in AI systems, accompanied by suggestions for making systems that ensure the preservation of collective epistemic resources.

Reference:

Milano, S., & Prunkl, C. (2025). Algorithmic profiling as a source of hermeneutical injustice. Philosophical Studies, 182(1), 185-203. https://link.springer.com/article/10.1007/s11098-023-02095-2

3. Rebuilding Epistemic Infrastructure: Examining Communication Systems and Structural Conditions that Enable (or Prevent) Collective Understanding of Algorithmic Harms

The digital environments that are customised according to algorithms tend to separate people more and more into different and specially designed information bubbles, which in turn, reduce the amount of communication that can take place among the large groups of people who need to perform the act of sense-making together. Scholars have looked into different aspects like lack of clarity, estrangement, and the manipulation of algorithms; however, the literature has so far overlooked the more profound issue of a malfunctioning epistemic infrastructure that is unable to foster the shared acknowledgement and understanding of algorithmic harms research topics. The authors of this paper, Milano and Prunkl (2025), in their article in Philosophical Studies, bring to the surface this neglected matter, and they point out the necessity for comprehensive research to be conducted to find out in what ways communication systems in digital spaces collapse in contexts where algorithms play a mediating role.

Problem Statement:
The literature on this issue has been quite sparse, and research projects have not yet tried to elaborate in detail on how the characteristics of online platforms can hinder the process of building a shared conceptual resource that operates within digital communication systems. The reduced understanding, which is basically a gap, of the whole process of engulfing, formulating, and responding collectively to algorithmic injustices that are seen as such by individuals, is a direct consequence of this gap.

Research Gap:

Even though there is an increasing concern about the use of algorithms and their lack of transparency and the creation of information silos, a multifaceted framework that goes along with communication breakdown, platform design, and the decline of the community’s interpretative resources has not yet been developed. The studies done so far do not address the direct way that lack of epistemic infrastructure results in hermeneutical injustice in algorithmic scenarios.

Research Question:
What are the structural and communicative conditions that are required for the adequate epistemic infrastructure in algorithmically mediated environments, and how do their breakdowns lead to hermeneutical injustice?

Outcome:
The dissertation will create a framework that presents the communication principles, structural necessities, and design characteristics required for digital platforms to support the sharing of hermeneutical resources and thus, be less susceptible to new types of algorithmic harm that might arise.

Reference:

Milano, S., & Prunkl, C. (2025). Algorithmic profiling as a source of hermeneutical injustice. Philosophical Studies, 182(1), 185-203. https://link.springer.com/article/10.1007/s11098-023-02095-2

4. Enhancing Reliability in AI-Driven Forensics: Addressing Technical and Deterministic Limitations of Large Language Models in Digital Investigations

The application of large language models (LLMs) in digital forensic workflows is becoming more and more common due to the advantages they offer in natural language processing and automated evidence analysis. The downside of the current architectures, however, is that they are still hampered by major issues, such as the handling of non-textual artefacts being unreliable, the outputs being non-deterministic by nature, weak forensic specialisation, and high computational costs. These LLM limitations in investigations make it hard to trust LLM-based systems in the legal context, where evidential consistency and methodological transparency are very important. According to Mahar et al. (2025), who wrote in VAWKUM Transactions on Computer Sciences, the challenges are highlighted, and the necessity for a forensic-focused reliability framework is emphasised.

Problem Statement:
Digital forensics and machine learning investigations need predictability, reproducibility and multimodal competence, which are the criteria that existing LLM architectures do not meet. Their non-deterministic behaviour, inability to accept non-textual inputs, and being computationally demanding all contribute to the unacceptability of the evidence and the unreliability of the investigation.

Research Gap:

Despite the fact that the latest studies highlight the potential of LLMs in the field of forensics, there is still no overall framework that covers their technical weaknesses in the areas of determinism, multimodal integration, linguistic complexity, and scalability. Furthermore, the current literature has not yet established systematic proposals for the adaptation of LLMs so that they can meet the standards of forensic evidence.

Research Question:
What are the ways to overcome the technical shortcomings of LLMs—non-textual data processing, determinism, linguistic complexity handling, and scalability?

Outcome:
The thesis is expected to present a technical reliability framework that will consist of determinism-enhancing strategies, architectural improvements, multimodal processing capabilities, and pathways of resource optimisation to adapt LLMs for stable, evidence-ready forensic use.

Reference:

Mahar, M. A., Raza, A., uddin Shaikh, Z., Burdi, A., Shabbir, M., & Iftikhar, M. (2025). Transformative Role of LLMs in Digital Forensic Investigation: Exploring Tools, Challenges, and Emerging Opportunities. VAWKUM Transactions on Computer Sciences, 13(1), 217-229. https://www.vfast.org/journals/index.php/VTCS/article/view/2127

5. Accountability in AI-Assisted Forensics: Developing Ethical, Legal, and Interpretability Frameworks for LLM Integration in Digital Investigations

The deployment of large language models (LLMs) in the field of digital forensics has raised a lot of questions about ethical issues like fairness, privacy, interpretability, and legal responsibility. As AI-generated results start to dictate the evidential processes, the aforementioned problems—algorithmic bias, lack of transparency, unclear liability, and threats to data protection—will have a strong impact on the acceptance and also on the ethical legitimacy of the technology. According to Mahar et al. (2025) in their paper published in VAWKUM Transactions on Computer Sciences, these threats must be faced, and the authors believe that the forensic environments need stronger accountability structures than the ones that AI governance literature provides at present, emphasising the urgent need for AI accountability in investigations.

Problem Statement:
The use of LLMs in forensic investigations presents the need for legal and ethical governance frameworks that are not only comprehensive but also very clear when it comes to the division of responsibility, the measures taken to avoid bias, and the observance of privacy laws such as GDPR and other data protection procedures that are in place.

Research gap:
The actual academic literature acknowledges the ethical problems of AI but does not come up with a single accountability model that would be suitable for digital forensics. The current models do not combine moral standards, legal obligations, explainability requirements, and bias-mitigation strategies into a unifying system that is appropriate for the practice of forensics.

Research Question:
What are the necessary ethical, legal, and interpretability safeguards to ensure accountability, fairness, and regulatory compliance in the implementation of LLMs in the digital forensic investigation process?

Outcome:
The thesis will create a governance and accountability framework that integrates fraud prevention, juristical endorsement, and transparent portrayal. This model will clearly delineate human–AI responsibility boundaries, reinforcing ethical AI in digital forensics.

Reference:

Mahar, M. A., Raza, A., uddin Shaikh, Z., Burdi, A., Shabbir, M., & Iftikhar, M. (2025). Transformative Role of LLMs in Digital Forensic Investigation: Exploring Tools, Challenges, and Emerging Opportunities. VAWKUM Transactions on Computer Sciences, 13(1), 217-229. https://www.vfast.org/journals/index.php/VTCS/article/view/2127

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