Assessing the Impact of Large Language Models on the Scalability and Efficiency of Automated Feedback Mechanisms in Massive Open Online Courses

The rapid proliferation of Massive Open OnlineCourses (MOOCs) offers particular difficulties in providingtimely and high-quality personalized feedbacks associated withcustomer interactions at scale. This research examines the gapwhich Large Language Models (LLMs) address with focus onautomation in providing timely feedback and the scalabilityefficiencies of LLMs in the feedback scope provided in MOOCsettings. Adopting a results-oriented experimental approach tofeedback systems, LLMs like GPT-3.5 and GPT-4 areimplemented across varying course contexts and learninggroups. Their outputs are benchmarked against traditionalsystems through semantic similarity calculations, response timemeasurement, cost evaluation, and learner satisfaction metrics.LLMs’ ability to comply with instructor feedback whileimproving responsiveness and personalization outpacedtraditional methods in every context analyzed, with satisfactionscores outperforming pre-set benchmarks across the board.Learners reported appreciation towards AI responses, citingenhanced understanding and interaction, overshadowed bydefendable claims of bias, genericity, and flawed constituentpressure. All in all, the study provides concrete guidanceillustrating the ways in which LLMs reconfigure pedagogicalfeedback mechanisms alongside MOOCs, shaping subsequentshifts in the design and integration strategies utilized in elearningframeworks across the world.

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