• AI-enabled DSS design and validation emerged, integrating knowledge-based systems, expert rules, and validation frameworks to assist managerial diagnosis and decision support [4], [12], [17], [2], [15].

• Group and collaborative DSS research emphasized combining communication, structured problem framing, and decision-support tools to improve ill-structured group problem solving [1], [20], [7], [11].

• Multicriteria decision making and method choice were scrutinized through experiments and comparative studies, highlighting user preferences, method sensitivity, and dynamic MCDM architectures [5], [19], [16], [9], [13].

• Human factors and cognitive dynamics of DSS use showed decision aids altering problem-solving processes, induced restrictiveness, UI issues, and user acceptance of complex tools [8], [10], [11], [13].

• Architectures for intelligent DSS emerged: flexible data/model management, hybrid AI approaches, and integration of neural networks with symbolic decision models for scheduling and real-time support [9], [18], [6], [20].