Participation in AI-Assisted Digital Evidence Identification Under Supervision: Learning, Reflection, and Practical Challenges in Low- Resource Investigative Environments

The role of digital evidence in modern investigations has expanded significantly due to the widespread use of digital devices and online services. From mobile phones and laptops to cloud platforms and social media, digital traces now form a central component of criminal, cyber, and civil investigations. However, many investigative agencies, particularly those operating in lowresource environments, struggle to manage and analyze digital evidence effectively. These challenges arise from limited access to advanced forensic tools, insufficient technical infrastructure, lack of trained personnel, and increasing complexity of digital data. Artificial intelligence (AI) has emerged as a potential supportive technology in digital forensics. AI-based methods can assist investigators by automating repetitive tasks, sorting large datasets, detecting patterns, and prioritizing potentially relevant evidence. While AI offers promising benefits, it also introduces technical, ethical, and legal challenges especially in environments where oversight and resources are limited. This research paper adopts a narrative and reflective approach to explore the use of AI in digital evidence identification within low-resource investigative settings. In addition, it reflects on the learning experience gained through participation in this research under academic supervision. Rather than viewing AI as a replacement for human investigators, the paper positions AI as a supportive tool that must operate alongside human judgment and ethical responsibility. The study highlights that supervised research plays a vital role in developing not only technical understanding but also critical thinking, ethical awareness, and professional maturity among students in cybersecurity and digital forensics.