Modelling AI Acceptance in Digital Forensic Studies through Correlational Analysis and Regression Metrics

DOI

10.2991/978-94-6239-610-4_14How to use a DOI?

Keywords

Artificial Intelligence; Technology Acceptance Model. Ethical Sensitivity; Responsible Trust; Academic Curricula

Abstract

Artificial Intelligence (AI) in digital forensic investigations is reshaping evidence analysis, and decision-making. However, the readiness of emerging forensic professionals to adopt these technologies remains underexplored. This cross-sectional study has investigated the awareness, acceptance, and ethical perceptions of AI adoption in digital forensics among academicians from forensics and allied disciplines. A structured questionnaire, based on Technology Acceptance Model (TAM) and its extended constructs, has been administered to a purposive sampling via google form. The questionnaire has measured key dimensions including awareness, perceived usefulness (PU), perceived ease of use (PEOU), social influence (SI), ethical concerns (EC), trust, and behavioural intention (TBI) using five-point Likert scales. A total of 200 participants from forensic sciences, computer sciences, and cyber/digital forensics domains have responded and completed the survey. Response data has been coded and analyzed in Microsoft Excel, 2019 using spearman correlation and simple linear regression. All 5 regression models have shown statistically significant positive relationships with awareness (p < 0.001). The explained variances (R²) have highlighted SI as the strongest predictor (R² = 0.4096), followed by PEOU (R² = 0.3765), TBI (R² = 0.2844), PU (R² = 0.2529), and EC (R² = 0.2205). These key findings have indicated that, higher awareness strongly enhances perceived ease of usefulness, perceived usefulness, social influence along with ethical sensitivity, and intention to adopt AI-based forensic tools. Additionally, EC score has shown a strong correlation with TBI (R = 0.63; R² = 0.3974; p < 0.001), underlining a major finding that, ethical sensitivity promotes responsible trust rather than upward rejection of AI. This study has highlighted the necessity for structured AI literacy, ethics modules, and institutional support in digital forensic academic curricula.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Ishita Chauhan
AU  - Bhavika Moza
AU  - Charu Saklani
AU  - Debhjit Mukherjee
PY  - 2026
DA  - 2026/05/05
TI  - Modelling AI Acceptance in Digital Forensic Studies through Correlational Analysis and Regression Metrics
BT  - Proceedings of the First International Conference on Advances in Forensics and Cyber Technologies (ICFACT 2025)
PB  - Atlantis Press
SP  - 122
EP  - 133
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-610-4_14
DO  - 10.2991/978-94-6239-610-4_14
ID  - Chauhan2026
ER  -