인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Abstract Background The increasing capabilities of generative artificial intelligence (AI), exemplified by OpenAI’s transformer-based language model GPT-4 (ChatGPT), have drawn attention to its application in educational contexts. This study evaluates the potential of such models in generating German reading comprehension texts for educational large-scale assessments, within the multilingual context of Luxembourg. Addressing the challenges faced by item developers in sourcing or manually developing numerous suitable texts, the study aims to determine if ChatGPT can assist text creation while maintaining high-quality standards. Methods The study employed a mixed-methods approach. In a qualitative focus group discussion, experts identified the strengths, weaknesses, opportunities and threats (SWOT) of using GPT-4 for text generation. These insights informed the construction of a Text Analysis Cognitive Model (TACM), which served as theoretical foundation. Narrative and informative reading comprehension texts were then generated using two distinct prompt engineering techniques, derived from original passages and TACM specifications. In a blinded online review, N = 89 participants evaluated human-written and AI-generated texts with regard to their readability, correctness, coherence, engagement and adequacy for reading assessment. Results All administered texts were of similarly high quality, with reviewers being unable to consistently identify authorship origins. Quantitative evaluations indicated that one-shot prompts are effective for creating high-quality informative texts, whereas human-written texts remain superior for narratives. Zero-shot prompts offer considerable flexibility and creativity, but still require human refinement. Conclusion These findings offer promising first insights into GPT-4’s capacity to emulate human-written texts which can be used in the large-scale assessment context. The considerable potential of using generative AI-models as a flexible and efficacious assistant in the creation of reading comprehension texts is highlighted. Still, the necessity of human oversight is emphasized through an augmented intelligence-driven perspective. Given the jurisdictional framework of the European Union, an effective implementation of ChatGPT in the test development process remains hypothetical at this time but is likely to change.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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