AI’s Capability in Understanding Text Context: Students’ Experiences

This study is aimed at investigating the capability of Artificial Intelligence (AI) in understanding the context on text, based on the users' experience. The research question is formulated: to what extent can AI understand and incorporate contextual context when generating text, through the experiences of participants. The methodology for this study follows a qualitative case study design, as outlined by Baxter and Jack (2008), which allows for an in-depth exploration of the phenomenon—AI's capability in understanding context, through participants' experiences. Data collection is conducted using two primary techniques: questionnaires, to gather quantitative data on participants' perceptions of AI's contextual understanding, and interviews, to obtain deeper qualitative insights. The data from the questionnaires will be analyzed using content analysis, which will categorize and quantify participants' responses. The results of the interviews revealed patterns and meanings related to how AI processes and understands context. The findings have practical consequences for developers and users of AI text-generation software.

Authors

Institutions

Publication Details

Journal
LET Linguistics Literature and English Teaching Journal
Published
2024-12-24
DOI
https://doi.org/10.18592/let.v14i2.14316
Primary Topic
Online Learning and Analytics
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

AI’s Capability in Understanding Text Context: Students’ Experiences

Benni Ichsanda Rahman Hz
LET Linguistics Literature and English Teaching Journal
Online Learning and Analytics
article

AI’s Capability in Understanding Text Context: Students’ Experiences

Benni Ichsanda Rahman Hz
article en

Abstract

This study is aimed at investigating the capability of Artificial Intelligence (AI) in understanding the context on text, based on the users' experience. The research question is formulated: to what extent can AI understand and incorporate contextual context when generating text, through the experiences of participants. The methodology for this study follows a qualitative case study design, as outlined by Baxter and Jack (2008), which allows for an in-depth exploration of the phenomenon—AI's capability in understanding context, through participants' experiences. Data collection is conducted using two primary techniques: questionnaires, to gather quantitative data on participants' perceptions of AI's contextual understanding, and interviews, to obtain deeper qualitative insights. The data from the questionnaires will be analyzed using content analysis, which will categorize and quantify participants' responses. The results of the interviews revealed patterns and meanings related to how AI processes and understands context. The findings have practical consequences for developers and users of AI text-generation software.

LET Linguistics Literature and English Teaching JournalVol. 14(2)
State University of Medan (ID)
Openalex Percentile: Top 72%
Online Learning and Analytics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.