Fangfang Mo

Traditional identification approaches have often excluded many students from underrepresented backgrounds from gifted and talented service programs. This study introduces an innovative identification method based on the Bull’s Eye Model for Affective Development—Expansion (BEM-e), which focuses on identifying giftedness and talents through targeted positive psychological traits. This method is integrated within an affective curriculum designed to create authentic learning environments that align with students’ interests and strengths, fostering deeper engagement, motivation, and self-efficacy. Grounded in the positive psychology theory, the curriculum includes engaging, activity-oriented modules with comprehensive dynamic assessments. These assessments allow teachers to identify a broad range of talents and abilities, promoting equity and a holistic identification process, which contributes to a more equitable and comprehensive education system.

ChatGPT has surprised academia with its remarkable abilities but also raised substantial concerns regarding academic integrity and misconduct. Despite the debate, empirical research exploring the issue is limited. The purpose of this study is to bridge this gap by analyzing Twitter data to understand how academia is perceiving ChatGPT. A total of 9733 tweets were collected through Python via Twitter API in three consecutive weeks in May and June 2023; and 3000 most relevant ones were analyzed in Atlas ti. 23. Our findings reveal a generally supportive attitude towards using ChatGPT in academia, but the absence of clear policies and regulations requires attention. Discussions primarily focus on academic integrity, learning effectiveness, and teaching efficiency. Tweets from influencers with over one million followers were analyzed separately. The significance of these findings and the limitations of the study are included.