Nlp on AI and Society Course

1.1 Automated Essay Scoring (AES)

Thu, 21 May 2026 00:00:00 +0000

Automated Essay Scoring (AES)

Context & Systems Architecture

Automated Essay Scoring (AES) systems utilize natural language processing (NLP) pipelines—ranging from traditional feature-extraction regression models (such as ETS’s e-rater) to fine-tuned transformer networks (such as BERT variants)—to evaluate student writing. These systems are widely deployed by major assessment corporations, including Pearson, ETS, and Turnitin, to grade high-volume standardized tests, admissions essays, and placement exams.

In a significant recent escalation of this technology, the Texas Education Agency deployed an automated scoring engine in 2024 to grade the State of Texas Assessments of Academic Readiness (STAAR) exams. The agency projected that the system would save $15–$20 million annually by reducing the number of human temporary graders from roughly 6,000 to under 2,000. However, the rollout resulted in widespread school district complaints after a dramatic surge in zero-scores for responses that did not fit the model’s expected syntax paths.

2.3 Mental Health Chatbots

Thu, 21 May 2026 00:00:00 +0000

Mental Health Chatbots

Context & Systems Architecture

Driven by acute clinician shortages and the low scalability of human therapy, conversational mental health applications (such as Woebot, Wysa, and specialized wellness tools) have expanded rapidly. These applications deliver automated Cognitive Behavioral Therapy (CBT) frameworks, mindfulness prompts, and mood-tracking exercises via automated interfaces. While marketed using medical language, these platforms are legally structured as “wellness software” to navigate around rigid healthcare regulations and liability standards.

6.1 AI Resume Screening & Hiring Discriminators

Thu, 21 May 2026 00:00:00 +0000

AI Resume Screening & Hiring Discriminators

Context & Systems Architecture

Automated hiring and talent acquisition tools are widely used by corporate Human Resources departments to filter through thousands of applicant resumes. Between 2014 and 2017, Amazon developed a proprietary, secret AI recruiting engine designed to automatically rank job applicants and identify top talent. The project was ultimately abandoned after internal engineers discovered that the machine learning pipeline had developed a systematic, algorithmic hostility toward female candidates, uncovering the core vulnerability of training models on historical human decision-making data.