Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

Texto completo

The rapid adoption of generative artificial intelligence (GenAI) technologies in higher education has raised concerns about academic integrity, assessment practices and student learning. Banning or blocking GenAI tools has proven ineffective, and punitive approaches ignore the potential benefits of these technologies. As a result, assessment reform has become a pressing topic in the GenAI era. This paper presents the findings of a pilot study conducted at British University Vietnam exploring the implementation of the Artificial Intelligence Assessment Scale (AIAS), a flexible framework for incorporating GenAI into educational assessments. The AIAS consists of five levels, ranging from “no AI” to “...

Ver ficha completa

Tipo: artículo

Metodología: trabajo teórico

Temas: inteligencia artificial

Resumen:

Texto completo

Higher education institutions, educators, and students continue to grapple with the wide-ranging implications of Artificial Intelligence (AI) and Generative Artificial Intelligence (GenAI). The ability of advanced GenAI models to complete educational assessments continues to be one of the most pressing issues for academia. In early 2024, we published the AI Assessment Scale (AIAS), which describes a practical framework for addressing GenAI use in educational assessment. The AIAS was well received and has been implemented in hundreds of institutions worldwide and translated into 30 languages. Building on our experience using the AIAS and drawing on feedback and critiques received...

Ver ficha completa

Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

Texto completo

This study investigates the efficacy of six major Generative AI (GenAI) text detectors when confronted with machine-generated content that has been modified using techniques designed to evade detection by these tools (n=805). The results demonstrate that the detectors' already low accuracy rates (39.5%) show major reductions in accuracy (17.4%) when faced with manipulated content, with some techniques proving more effective than others in evading detection. The accuracy limitations and the potential for false accusations demonstrate that these tools cannot currently be recommended for determining whether violations of academic integrity have occurred, underscoring the challenges educators face in maintaining inclusive and...

Ver ficha completa