Smart Evidence Management: Concept, Design and Evaluation of an AI-Assisted Approach for Automotive SPICE Assessments

Automotive SPICE® (ASPICE) conformance activities require systematic discovery and evaluation of project documentation against standardised process indicators, consuming an estimated 40–60% of assessment preparation time with high extraneous cognitive load. This paper presents Smart Evidence Management (SEM), a principled Human–AI Collaboration (HAIC) approach grounded in Cognitive Load Theory, Trust in Automation, and Situation Awareness theory. SEM defines four design principles—AI-assisted discovery, expert-validated judgement, transparent reasoning, and full data sovereignty—as a novel generalisable HAIC pattern for documentary evidence evaluation in regulated professional domains. By reducing evidence-hunting effort, SEM operationalises the Plan-Do-Check-Act (PDCA) continuous improvement principle, enabling iterative conformance gap detection throughout development rather than only at formal assessment events. The Smart Evidence Manager, a prototype instantiation for ASPICE Process Assessment Model (PAM) v4.0, implements a two-stage hybrid symbolic–neural Retrieval-Augmented Generation (RAG) architecture with fully local Large Language Model (LLM) inference. An expert agreement study (n = 120 findings, five intacs® certified assessors) yielded a combined acceptance rate of 90.0% (95% Confidence Interval (CI) [83.3%, 94.2%]). A beta-test study is under recruitment (target n ≥ 30 practitioners) to quantify cognitive augmentation effects, usability, and trust calibration. Cultural dimensions of trust calibration are discussed, extending SEM to intercultural deployment contexts.