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In education, artificial intelligence (AI) plays an increasingly important role and is contributing to the growth of more adaptive, interactive, and personalized forms of learning. This paper considers this evolution from AI-based education toward cognitive education, from three main dimensions: educational applications, AI technologies, and intelligent educational systems. Based on the review of the literature, the paper first presents the main applications of AI in education such as personalized learning, teaching and instructional support, student monitoring and academic integrity, and experiential and immersive learning. Then, it reviews the main technologies used to support these applications, such as machine learning and deep learning, natural language processing, computer vision, knowledge representation and reasoning, and more recent developments in generative AI. The study also examines how these applications and technologies are implemented in intelligent educational systems and platforms. A cross-dimensional analysis examines the relationships between these three dimensions. Complementing this analysis, we propose six functional orientations based on the conceptual synthesis and examine six cognitive capabilities: perception, learning, context awareness, reasoning, adaptation, and interaction. The proposed framework connects the three dimensions and the six capabilities within a continuous feedback loop. Cognitive Education is thus presented as a conceptual research direction requiring empirical validation. Finally, the article discusses the main technical, pedagogical, ethical, and governance challenges, along with emerging trends and directions for future research. Finally, the article discusses the main technical, pedagogical, ethical, and governance challenges and presents emerging trends and possible directions for future research.

Identifying employees who may need additional professional-learning support could help organisations allocate training resources, but model comparisons in this area often omit trivial baselines and class-specific metrics. We compare seven classifiers (decision tree, random forest, logistic regression, k-nearest neighbours, XGBoost, LightGBM, CatBoost) with a rule-based and a majority-class baseline under one protocol: stratified hold-out, the same tuning procedure, paired bootstrap confidence intervals, a descriptive repeated cross-validation check, and SHAP and LIME explanations. Experiment 1 predicts a KPI label of undocumented origin for 17,415 employees (35.9% positive); it is an illustrative comparison using the label as distributed, not validated evidence about KPI attainment. Experiment 2 predicts promotion for 54,808 employees (8.5% positive), a related task from the same public source and not an external validation. In Experiment 1, learning models reached macro-F1 of 0.628 to 0.663, against 0.590 for the rule-based baseline; a depth-5 decision tree was 0.005 below the reference random forest (95% bootstrap CI −0.016 to +0.005) but was not shown non-inferior at a 0.01 margin. In Experiment 2, boosting models were best (macro-F1 0.734 to 0.739), the decision tree was clearly worse (−0.030), and only about 35% of promoted employees were identified. Associations with training indicators are not causal, and no effect on decisions was tested.

In content-based recommendation, a second-stage (L2) reranker decides which retrieved items fill the first screen. Decision models, which answer a typed question about a text with a probability, are an alternative to trained cross-encoders, but whether they separate exact matches from close substitutes is unknown. We cast a decision model as a pointwise reranker that asks whether each candidate exactly matches the request. Under a pre-registered protocol, a gain must hold on both retrieved and fully labelled lists. On Amazon ESCI (1000 requests) and Wayfair WANDS (299), the commercial Jev model, used zero-shot, improves P@10 over embedding order by 0.046 to 0.054 and beats the 0.6B Qwen3-Reranker by 0.02 to 0.03 on ESCI, but not significantly on WANDS. Larger trained cross-encoders (Qwen3-Reranker-4B and 8B, bge-reranker-v2-m3) erase most of this lead: Jev is ahead only on fully labelled ESCI lists. A prompted Qwen3-8B stays behind Jev everywhere. The open decision model CLM-v0.1-8B ranks at random level when scoring a whole list but matches embedding order one product at a time. Jev costs about as much as the 4B model and 13 times bge-reranker-v2-m3. A decision model can serve as an L2 reranker but not demonstrably better than trained cross-encoders.