TY - CONF

T1 - Higher Education Assessment in the Era of Artificial Intelligence

AU - Stockton, Glynn

PY - 2023/11/1

Y1 - 2023/11/1

N2 - Following OpenAI’s publically-accessible release of a demonstration version of ChatGPT-3 on 30 November 2022, the software went viral and attracted one million users within the first five days (Marr, 2023), growing exponentially to over one hundred million active monthly users within sixty days (T. Wu et al., 2023). The arrival of the technology created strong reactions from corporations, educational institutes and governments. In particular, Higher Education Institutions were not prepared for neither the scale of access nor the capabilities of the Large Language Models.In an attempt to protect the integrity of summative assessments, some institutions have reverted to methods which are deemed to be safe from the impact of Generative Artificial Intelligence, but these methods are typically rated as “Stressful” or “Very Stressful” by students (Kent-Waters et al, 2018).This poster contrasts the assessment modes potentially impacted by GAI with the fifty-eight assessment methods currently utilized in Higher Education in the UK as identified by Kent-Waters et al (2018) and surmises which modes have the potential for the best compromise between authentic assessment and student mental health.This meta-synthesis develops a new theory of authentic assessment in an era of GAI with consideration of the student experience for optimal outcome.

AB - Following OpenAI’s publically-accessible release of a demonstration version of ChatGPT-3 on 30 November 2022, the software went viral and attracted one million users within the first five days (Marr, 2023), growing exponentially to over one hundred million active monthly users within sixty days (T. Wu et al., 2023). The arrival of the technology created strong reactions from corporations, educational institutes and governments. In particular, Higher Education Institutions were not prepared for neither the scale of access nor the capabilities of the Large Language Models.In an attempt to protect the integrity of summative assessments, some institutions have reverted to methods which are deemed to be safe from the impact of Generative Artificial Intelligence, but these methods are typically rated as “Stressful” or “Very Stressful” by students (Kent-Waters et al, 2018).This poster contrasts the assessment modes potentially impacted by GAI with the fifty-eight assessment methods currently utilized in Higher Education in the UK as identified by Kent-Waters et al (2018) and surmises which modes have the potential for the best compromise between authentic assessment and student mental health.This meta-synthesis develops a new theory of authentic assessment in an era of GAI with consideration of the student experience for optimal outcome.

KW - Alternative Assessment

KW - Generative Artificial Intelligence

KW - Student Wellbeing

KW - Learning Outcomes

M3 - Poster

T2 - Sunway University’s 2nd Teaching and Learning Poster Competition

Y2 - 1 November 2023

ER -