Computer-aided design (CAD) systems have advanced to become a critical tool in product design. Nevertheless, they still primarily rely on the traditional mouse and keyboard interface. This limits the naturalness and intuitiveness of the 3D modeling process. Recently, a multimodal human–computer interface (HCI) has been proposed as the next-generation interaction paradigm. Widening the use of a multimodal HCI provides new opportunities for realizing natural interactions in 3D modeling. In this study, we conducted a literature review of a multimodal HCI for CAD to summarize the state-of-the-art research and establish a solid foundation for future research. We explore and categorize the requirements for natural HCIs and discuss paradigms for their implementation in CAD. Following this, factors to evaluate the system performance and user experience of a natural HCI are summarized and analyzed. We conclude by discussing challenges and key research directions for a natural HCI in product design to inspire future studies.

With the increasing demand for natural interactions, people have realized that an intuitive Computer-Aided Design (CAD) interaction mode can reduce the complexity of CAD operation and improve the design experience. Although interaction modes like gaze and gesture are compatible with some complex CAD manipulations, they still require people to express their design intentions physically. The brain contains design intentions implicitly and controls the corresponding body parts that execute the task. Therefore, building an end-to-end channel between the brain and computer as an auxiliary mode for CAD manipulation will allow people to send design intentions mentally and make their interaction more intuitive. This work focuses on the 1-D translation scene and studies a spatial visual imagery (SVI) paradigm to provide theoretical support for building an electroencephalograph (EEG)-based brain–computer interface (BCI) for CAD manipulation. Based on the analysis of three spatial EEG features related to SVI (e.g., common spatial patterns, cross-correlation, and coherence), a multi-feature fusion-based discrimination model was built for SVI. The average accuracy of the intent discrimination of 10 subjects was 86%, and the highest accuracy was 93%. The method proposed was verified to be feasible for discriminating the intentions of CAD object translation with good classification performance. This work further proves the potential of BCI in natural CAD manipulation.

Adaptive learning systems need to sequence learning activities according to individual progress while respecting the prerequisite structure of the learning domain. This study proposes the Triadic Educational Knowledge Space (TEKS) framework for target-oriented adaptive sequencing in structured domains. TEKS integrates a prerequisite graph, a task-specific target set, and a recursively updated predictive distribution over prerequisite-consistent latent knowledge states. At each decision step, candidate items are evaluated using their predictive one-step acquisition probability, adjusted by graph-based target relevance. A simulation-based proof-of-concept study evaluated TEKS on an eight-node symmetric branching graph with two isomorphic target conditions, a six-node asymmetric converging graph, and a six-node linear prerequisite chain. Each condition involved 200 virtual learners, 90 interaction steps, and 30 independent repetitions. TEKS was compared with Prerequisite-Weighted Mastery Gap (PW-MG), a deterministic static schedule, and random selection under matched simulation conditions. In the branching structures, TEKS yielded higher final target mastery, mean target mastery over time, and final scope mastery than all three comparison strategies. In the linear prerequisite chain, TEKS and PW-MG showed similar target-level outcomes, whereas TEKS retained higher final scope mastery. Within the examined conditions, the difference between TEKS and PW-MG was most evident when multiple prerequisite-related candidates competed for selection. These results provide controlled proof-of-concept evidence for target-adjusted one-step acquisition scoring. Further evaluation is needed on larger and more varied prerequisite structures, under broader forms of model misspecification, and with authentic learner interaction data.