Generative AI Policies

This policy is based on and refers to the guidelines outlined in the Generative AI Policies for Journals, as provided by (1)  Elsevier;  (2) COPE Focus on artificial intelligence (3)  guidebook for the use of generative artificial intelligence in learning in higher education; (4)  STM: Recommendations for classifying AI use in academic manuscript preparation; (5) WAME : Chatbots, generative AI, and scholarly manuscripts

Indonesian Journal of Learning Education and Counseling understands the importance of artificial intelligence (AI) and its potential to help authors with their research and writing processes. Indonesian Journal of Learning Education and Counseling is excited about the new possibilities that generative AI tools bring, especially for helping to come up with ideas, speed up research, analyze results, improve writing, organize submissions, assist authors who write in a second language, and speed up the research and sharing process.  Indonesian Journal of Learning Education and Counseling is offering guidance to authors, editors, and reviewers on the use of such tools, which may evolve given the swift development of the AI field.  

Generative Artificial Intelligence (AI) tools, including large language models (LLMs) and multimodal models, are continually developing and evolving, particularly in their applications for businesses and consumers. While generative AI possesses significant potential to enhance creativity for authors, it is important to acknowledge the associated risks that come with the current generation of these tools. Generative AI can produce a wide variety of content, encompassing text generation, image synthesis, audio, and synthetic data. Notable examples of such tools include ChatGPT, Copilot, Gemini, Claude, NovelAI, Jasper AI, DALL-E, Midjourney, and Runway. 

Some of the risks associated with the operation of generative AI tools today are:

  1. Inaccuracy and bias: Generative AI tools are fundamentally statistical in nature rather than factual. Consequently, they can introduce inaccuracies, falsehoods (often referred to as hallucinations), or biases that may be difficult to detect, verify, and rectify.
  2. Lack of attribution: Generative AI frequently fails to adhere to the established practices within the global scholarly community regarding the correct and precise attribution of ideas, quotes, or citations.
  3. Confidentiality and intellectual property risks: Currently, generative AI tools are often employed on third-party platforms that may not provide adequate standards for confidentiality, data security, or copyright protection.
  4. Unintended uses: Providers of generative AI may repurpose input or output data generated from user interactions (for instance, for AI training). This practice has the potential to infringe upon the rights of authors and publishers, among others.