Page 85 - AGRANI SAMKALP SILVER JUBILEE SOUVENIR

z
vx.kh ladYi jtr t;arh Lekfjdk

“proactive learners,” and
“assignment delegators”-
based on their self-reported
reliance on ChatGPT for
various learning tasks.
Perception Analysis:
To understand broader
perceptions, experiences,
and concerns surrounding
ChatGPT in education,
researchers are analyzing
publicly available data on
social media platforms.
Through qualitative content
analysis of posts, comments,
and discussions on
platforms like Twitter,
Reddit, YouTube, and LinkedIn, researchers can gain insights focusing on competence and performance. This new
into how ChatGPT is used and perceived by diverse paradigm emphasizes practical application, critical analysis,
stakeholders, including students, educators, parents, and the and creative problem-solving, moving beyond the limitations
general public. This method reveals both the perceived of AI-generated content. The focus of assessments would
benefits and potential drawbacks of ChatGPT as an shift from simply “knowing what” to “knowing how” and
educational tool. “showing how.”
Skill Taxonomy Analysis: To gain a deeper Concluding Remarks and Future Perspectives
understanding of user perceptions of ChatGPT's impact on The integration of AI technologies like ChatGPT into
specific skills, researchers are turning to sentiment analysis education is still in its early stages, and research on its impact
and natural language processing (NLP) techniques. This is rapidly evolving. The methods described above provide a
involves collecting large datasets of text data, such as tweets snapshot of the diverse approaches researchers are using to
related to ChatGPT, and using NLP algorithms to identify examine this emerging phenomenon. These studies are crucial
tasks that users are asking ChatGPT to perform. These tasks for informing the development of ethical guidelines, effective
are then compared with established skill taxonomies to pedagogical strategies, and institutional policies related to
determine which skills are perceived as most impacted by AI in education.
AI. Sentiment analysis can reveal whether users view this Future research should continue to investigate the long-
impact positively or negatively. term effects of LLMs on student learning, motivation, and
Insights for Educators skills development. It is crucial to examine the ethical
The emergence of LLMs necessitates a reassessment of considerations surrounding AI use in education, such as
traditional evaluation methods in education. Its ability to potential biases, academic integrity issues, and the impact
generate high-quality, original content challenges the validity on student-teacher relationships. Further exploration of the
of existing assessment approaches. Some researchers propose interplay between AI tools, pedagogical approaches, and
shifting from knowledge-based assessments to evaluations student learning styles is also warranted. 

83