Comparison of AI-Based HCI Modalities for Selecting Interaction Systems in Sustainable Manufacturing

Human–computer interaction (HCI) has evolved from traditional command-based interfaces to adaptive systems powered by artificial intelligence (AI). In industrial environments, particularly manufacturing and logistics, selecting the appropriate interaction modality is crucial for efficiency, safety, and user acceptance. This study presents a conceptual decision support framework that analyzes three modalities—visual, voice, and multimodal—based on a systematic literature review covering the period from 2003 to early 2026. The analysis evaluates differences in usability, cognitive workload, implementation complexity, and operational benefits of HCI and AI-based HCI. To address the selection challenge, a multi-criteria decision analysis (MCDA) model was developed. The proposed MCDA model is based on a structured literature analysis and expert-informed evaluation. The expert-based MCDA ranking is context-dependent and grounded in the reviewed literature. The results indicate that multimodal HCI shows the highest potential in manufacturing scenarios, offering advantages in safety, robustness, flexibility, and potential contributions to sustainability. However, it also indicates more demanding implementation, training requirements, and higher costs. The proposed decision support framework is intended to serve as a methodological tool for the structured evaluation of HCI modality suitability in sustainable manufacturing environments.