AI Assisted Case Analysis and Self Reported Evaluative Confidence among Law Students in Bangladesh
Year: 2026
Type: preprint
Abstract: Generative artificial intelligence can support legal case analysis, but perceived assistance, reported verification and confidence in evaluating reasoning represent different educational outcomes. This study examines their alignment in a Bangladesh questionnaire database containing 385 consenting, AI-aware adults. The primary analysis concerns the 237 respondents identifying as law students; the full database provides descriptive context and sensitivity analysis. Low internal consistency supported item-level analysis rather than a composite critical-reasoning scale. Endorsements, paired contrasts, joint response patterns and exploratory regression were used to distinguish reported practices from self-assessed capability. Among students, 66.7% reported checking AI-generated legal information against authoritative sources, 41.4% reported comparing alternative interpretations, 48.9% expressed confidence in detecting weak or unsupported reasoning and 52.3% reported retaining independent judgement. Verification endorsement exceeded detection confidence by 17.7 percentage points (bootstrap 95% interval 9.3–26.2; Holm-adjusted p < 0.001). Only 18.6% endorsed verification, detection confidence and independent judgement together. None of four focal predictors-comparison, legal-source verification, awareness of plausible inaccuracies and prompting capability-was retained after multiplicity correction in the adjusted student model. Ordinal, full-sample and secondary judgement-outcome analyses likewise retained no focal coefficient. Self-regulated learning supplies a process-oriented interpretation, while technology acceptance helps delimit perceptions of assistance. The findings identify uneven alignment among self-reports rather than an observed deficit in legal reasoning or a causal effect of AI. Educational implications concern evaluating source checks, argument comparison and independent revision through performance-based tasks, with explicit attention to jurisdiction and authority. The study supports more discriminating assessment of AI-assisted legal learning rather than inferring competence from use or confidence alone.
Source: Research Square
Language: English
Subfield: Law
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7225646033