The current English grammar teaching is generally characterized by the problems of single teaching method, insufficient personalized instruction, and lack of scientific assessment of teaching effect. The traditional teaching mode is difficult to adapt to the cognitive characteristics and learning needs of different learners, and teachers lack intelligent support in the selection of grammar knowledge points and teaching path design. Based on the higher-order cognitive computing framework, this study constructs an ant colony algorithm-driven intelligent instructional path design model for English grammar teaching. Adopting the improved clustering method of ant foraging principle, 100 English teachers were selected as the research samples, and an evaluation system containing a total of 12 characteristic parameters in four dimensions of teaching content, teaching ability, teaching attitude, and academic level was established. The two-part graph maximum matching algorithm and MMAS pheromone updating mechanism are used to design the intelligent instruction path generation strategy by combining the three elements of form, meaning and usage of Larsen-Freeman three-dimensional grammar teaching theory. The experimental results show that the improved algorithm achieves an accuracy of 86.13% in the recognition of Arank teachers and 89.69% in B-rank, which is 6.09% and 10.43% higher than the original algorithm, respectively. The effectiveness of the algorithm was verified through 8 iterations of clustering analysis on 10 teachers. The intelligent guidance system constructed by the study can automatically determine the structural, functional or combined structural-functional teaching features according to the teaching grammar attributes, provide a personalized path optimization plan for English grammar teaching, and improve the teaching quality and learning effect.
