数智技术, 人才评价, 人才发展, 情境行为证据, 整合性学习理论

Digital and intelligent technologies extend the evidence base of talent assessment by making behavior in structured situations recordable, interpretable, and usable for feedback, rather than by replacing established methods. Self- and other-reports, structured interviews, assessment center techniques, work samples, situational judgment tests, game-related assessments, virtual reality, and digital logs provide complementary information. Across these traditions, situated behavioral evidence refers to trajectories of choices, interactions, information search, resource allocation, and strategy adjustment under specified roles, goals, resources, constraints, and

feedback rules. It is not a new assessment format, but an evidence-organizing framework that links structured situations, behavioral traces, construct inference, and developmental feedback. Integrative learning theory explains how metacognitively guided whole–part–whole processing may reorganize assessment traces into developmental feedback and support transfer to real work. The resulting closed loop connects job-context modeling, task design, trace collection, inference, feedback, real-work transfer, and continuous calibration. Studies of automated scoring of open-ended situational judgment tests, game logs, natural language processing in operational selection, and virtual-reality bystander responses illustrate component-level operationalization and a testable construct—task—behavior—scoring—criterion pathway. These studies do not validate the framework as a whole. Responsible use requires job relevance, validity evidence, algorithmic fairness, data minimization, transparency, appeal mechanisms, and developmental ethics.

digital and intelligent technologies, talent assessment, talent development, situated behavioral evidence, integrative learning theory