#smrgKİTABEVİ Assessment in the Age of AI: Feedback, Scoring, and Validity in Higher Education -

Editör:
Kondisyon:
Yeni
Sunuş / Önsöz / Sonsöz / Giriş:
ISBN-10:
Hazırlayan:
Cilt:
Amerikan Cilt
Ciltçi:
Stok Kodu:
1199268475
Boyut:
16x24
Sayfa Sayısı:
78
Basım Yeri:
Ankara
Baskı:
1
Basım Tarihi:
2026
Kapak Türü:
Karton Kapak
Kağıt Türü:
Enso Kâğıt
Dili:
İngilizce
indirimli
162,00
Havale/EFT ile: 158,76
1199268475
656651
Assessment in the Age of AI: Feedback, Scoring, and Validity in Higher Education -
Assessment in the Age of AI: Feedback, Scoring, and Validity in Higher Education - #smrgKİTABEVİ
162
Generative AI is rapidly changing how higher education assesses learning: models now draft feedback on student writing, assign scores, and even explain their decisions. But does a system's resemblance to human judgment make it trustworthy? This book argues that it does not, treating assessment in the age of AI not as an accuracy problem to be solved by larger models, but as a human-centered socio-technical practice in which feedback must be designed, scoring must be governed, and claims must be validated.Across three connected parts, the book follows a single arc: designing AI feedback that students actually learn from; governing LLM-based scoring around privacy, security, fairness, and human oversight; and validating scores through defensible study design, reliability and bias evidence, and reproducible reporting. Written for researchers, instructors, and academic developers in educational technology and assessment, it offers not a tool to adopt but a way of thinking, one that keeps the students and teachers assessment serves at its center.
Generative AI is rapidly changing how higher education assesses learning: models now draft feedback on student writing, assign scores, and even explain their decisions. But does a system's resemblance to human judgment make it trustworthy? This book argues that it does not, treating assessment in the age of AI not as an accuracy problem to be solved by larger models, but as a human-centered socio-technical practice in which feedback must be designed, scoring must be governed, and claims must be validated.Across three connected parts, the book follows a single arc: designing AI feedback that students actually learn from; governing LLM-based scoring around privacy, security, fairness, and human oversight; and validating scores through defensible study design, reliability and bias evidence, and reproducible reporting. Written for researchers, instructors, and academic developers in educational technology and assessment, it offers not a tool to adopt but a way of thinking, one that keeps the students and teachers assessment serves at its center.
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