Search engine for discovering works of Art, research articles, and books related to Art and Culture
ShareThis
Javascript must be enabled to continue!

Mapping the Ethical Landscape of GenAI: Insights from Applied Linguistics Publication Policies

View through CrossRef
The swift rise of generative AI (GenAI) in 2022 has led to extensive acceptance in academic fields; yet, applied linguists have not achieved agreement on its ethical and suitable application in research. This study underscores the urgent necessity for enhanced GenAI literacy among scholars, especially those involved in composing research articles. We analyze 76 papers chosen from 170 high-impact applied linguistics journals to examine the scope and character of GenAI use guidelines. Three fundamental dimensions—authorship, use cases, and human responsibility—were addressed by the seventeen particular elements and four general requirements that comprised the structured checklist. The results indicate substantial discrepancies among journals. Only fifty percent provided guidelines linked to GenAI for authors, and the comprehensiveness and extent of these suggestions differed significantly. Significant discord existed concerning the applicability of GenAI technologies for functions such as idea generation, image or data creation, data collecting, analysis and interpretation, or manuscript composition. Moreover, the inconsistency in the declaration of GenAI usage further complicated ethical interaction with the technology. In light of these concerns, we suggest implementable solutions for journals to improve their GenAI-related policies and encourage responsible usage among authors. A new conceptual framework describing the competencies researchers need to navigate the ethical and transparent use of GenAI is introduced in our study, GenAI-LR, which is central to research article writing. This study offers pragmatic recommendations based on empirical evidence to assist scholars and editors in harmonizing GenAI practices with advancing academic norms.
International Journal of Innovative Science and Research Technology
Title: Mapping the Ethical Landscape of GenAI: Insights from Applied Linguistics Publication Policies
Description:
The swift rise of generative AI (GenAI) in 2022 has led to extensive acceptance in academic fields; yet, applied linguists have not achieved agreement on its ethical and suitable application in research.
This study underscores the urgent necessity for enhanced GenAI literacy among scholars, especially those involved in composing research articles.
We analyze 76 papers chosen from 170 high-impact applied linguistics journals to examine the scope and character of GenAI use guidelines.
Three fundamental dimensions—authorship, use cases, and human responsibility—were addressed by the seventeen particular elements and four general requirements that comprised the structured checklist.
The results indicate substantial discrepancies among journals.
Only fifty percent provided guidelines linked to GenAI for authors, and the comprehensiveness and extent of these suggestions differed significantly.
Significant discord existed concerning the applicability of GenAI technologies for functions such as idea generation, image or data creation, data collecting, analysis and interpretation, or manuscript composition.
Moreover, the inconsistency in the declaration of GenAI usage further complicated ethical interaction with the technology.
In light of these concerns, we suggest implementable solutions for journals to improve their GenAI-related policies and encourage responsible usage among authors.
A new conceptual framework describing the competencies researchers need to navigate the ethical and transparent use of GenAI is introduced in our study, GenAI-LR, which is central to research article writing.
This study offers pragmatic recommendations based on empirical evidence to assist scholars and editors in harmonizing GenAI practices with advancing academic norms.

Related Results

Generative Artificial Intelligence in Tertiary Level Education in Bangladesh: Practices, Benefits, Challenges, and Prospects
Generative Artificial Intelligence in Tertiary Level Education in Bangladesh: Practices, Benefits, Challenges, and Prospects
Aim/Purpose: This study aimed to investigate the potential of integrating Generative Artificial Intelligence (GenAI) in tertiary education. It examined current practices among teac...
Materialism and Environmental Knowledge as a Mediator for Relationships between Religiosity and Ethical Consumption
Materialism and Environmental Knowledge as a Mediator for Relationships between Religiosity and Ethical Consumption
ABSTRACTOn a global and regional scale, Indonesia has one of the least environmentally sustainable economies in the Asia-Pacific region. Consumption is one of the key factors contr...
Understanding the Ethics of Generative AI: Established and New Ethical Principles
Understanding the Ethics of Generative AI: Established and New Ethical Principles
This scoping review develops a conceptual synthesis of the ethics principles of generative artificial intelligence (GenAI) and large language models (LLMs). In regard to the emergi...
PREPARING AI SUPER USERS THROUGH GENERATIVE AI INTEGRATION IN EDUCATION
PREPARING AI SUPER USERS THROUGH GENERATIVE AI INTEGRATION IN EDUCATION
Peningkatan pesat Kecerdasan Buatan Generatif (GenAI) menghadirkan peluang dan tantangan bagi pendidikan tinggi, khususnya dalam mempersiapkan mahasiswa untuk menggunakan perangkat...
What does current GenAI actually mean for student learning?
What does current GenAI actually mean for student learning?
Many genAI (generative Artificial Intelligence) enthusiasts and much of the broader public see genAI as a substantial force for good within education. Unfortunately, some of those ...
Generative artificial intelligence in chemical engineering spans multiple scales
Generative artificial intelligence in chemical engineering spans multiple scales
Recent advances in generative artificial intelligence (GenAI), particularly large language models (LLMs), are profoundly impacting many fields. In chemical engineering, GenAI plays...
GenAI at the Edge: Comprehensive Survey on Empowering Edge Devices
GenAI at the Edge: Comprehensive Survey on Empowering Edge Devices
Generative Artificial Intelligence (GenAI) applies models and algorithms such as Large Language Model (LLM) and Foundation Model (FM) to generate new data. GenAI, as a promising ap...
A Response to "How AI Destroys Institutions"
A Response to "How AI Destroys Institutions"
In “How AI Destroys Institutions,” Professors Woodrow Hartzog and Jessica Silbey argue that Generative AI (GenAI) systems—by their very design—undermine expertise, short-circuit de...

Back to Top