Authors - Vasumathi R, Kalpana Y Abstract - Graduate communication competency gaps represent a critical barrier to the workforce readiness in the Indian higher education, yet existing assessment infrastructure measures a credential completion rather than the skill trajectories over time. This paper presents a LSTM-CDSF (Long Short-Term Memory Communication Demand and Skill Forecasting), a temporal deep learning based framework that predicts the future communication skill demand from the sequential monthly assessment records and also quantifies per skill gaps against the industry benchmarks. The framework operates on a synthetic dataset of 240 students observed over a period of 18 months calibrated to published NASSCOM and India Skills Report statistics. LSTM-CDSF achieves a Mean Absolute Error of 1.468, RMSE of 1.837, MAPE of 2.61%, and R² of 0.9249 on a held-out test set of 480 sequences, demonstrating consistent performance improvements over the Linear Regression, ARIMA, and a naïve baseline across all the evaluated metrics. Gap analysis reveals that the Digital Communication (gap: 25.4 points) and the Intercultural Communication (gap: 23.5 points) requires the most urgent curriculum interventions.
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