Standardized English proficiency tests such as the Pearson Test of English (PTE) Academic play a critical role in determining access to higher education, employment, and migration opportunities. Although integrated tasks such as the Summarize Group Discussion (SGD) task enhance communicative authenticity, they also impose substantial cognitive demands that are not adequately captured by conventional scoring models. Consequently, individual differences in cognitive processing efficiency may influence performance independently of linguistic proficiency, potentially threatening the validity and fairness of high-stakes language assessment. This study investigates whether Individual Alpha Frequency (IAF), a stable electroencephalographic (EEG) marker of neural efficiency, predicts Iranian English learners' performance on the PTE SGD task and their cognitive load beyond traditional language proficiency measures. Employing a multiphase mixed-methods design, the study will recruit 6–8 Iranian adult English learners. Resting-state EEG will measure IAF, followed by continuous EEG recording during the listening and speaking phases of the SGD task. Participants' oral summaries will be rated by two human raters and an AI-based scoring system, while subjective cognitive load will be assessed using the NASA Task Load Index (NASA-TLX) and follow-up qualitative interviews. Quantitative data will be analyzed using descriptive statistics, inter-rater reliability, non-parametric correlations, and within-participant EEG comparisons, whereas qualitative responses will contextualize learners' perceived cognitive load. The findings are expected to demonstrate the predictive value of neural efficiency for integrated task performance, informing more valid and equitable assessment practices while advancing cognitively informed approaches to language testing and English language instruction.