Artificial intelligence (AI) is already integrated into news production strategies in some media outlets. Recently, generative AIs such as ChatGPT and others have demonstrated their ability to enhance productivity in content production tasks, raising the question of how journalism faculties can address this new technology. This paper presents an academic study on the application of AI in higher communication studies. The study involved 4 in-depth interviews and 28 semi-structured interviews with university lecturers and researchers. The findings confirm varying degrees of convergence and divergence on different aspects of the technology, including the integration of AI in communication faculties, student training in AI usage, the introduction of AI and journalism as a subject area, and the potential uses of AI in news production and consumption. Additionally, this paper proposes a comprehensive training program on AI and journalism, focusing on its foundations, technical competencies and ethical considerations.

The visibility of academic articles or conference papers depends on their being easily found in academic search engines, above all in Google Scholar. To enhance this visibility, search engine optimization (SEO) has been applied in recent years to academic search engines in order to optimize documents and, thereby, ensure they are better ranked in search pages (i.e., academic search engine optimization or ASEO). To achieve this degree of optimization, we first need to further our understanding of Google Scholar’s relevance ranking algorithm, so that, based on this knowledge, we can highlight or improve those characteristics that academic documents already present and which are taken into account by the algorithm. This study seeks to advance our knowledge in this line of research by determining whether the language in which a document is published is a positioning factor in the Google Scholar relevance ranking algorithm. Here, we employ a reverse engineering research methodology based on a statistical analysis that uses Spearman’s correlation coefficient. The results obtained point to a bias in multilingual searches conducted in Google Scholar with documents published in languages other than in English being systematically relegated to positions that make them virtually invisible. This finding has important repercussions, both for conducting searches and for optimizing positioning in Google Scholar, being especially critical for articles on subjects that are expressed in the same way in English and other languages, the case, for example, of trademarks, chemical compounds, industrial products, acronyms, drugs, diseases, etc.

Search engine optimization (SEO) constitutes the set of methods designed to increase the visibility of, and the number of visits to, a web page by means of its ranking on the search engine results pages. Recently, SEO has also been applied to academic databases and search engines, in a trend that is in constant growth. This new approach, known as academic SEO (ASEO), has generated a field of study with considerable future growth potential due to the impact of open science. The study reported here forms part of this new field of analysis. The ranking of results is a key aspect in any information system since it determines the way in which these results are presented to the user. The aim of this study is to analyze and compare the relevance ranking algorithms employed by various academic platforms to identify the importance of citations received in their algorithms. Specifically, we analyze two search engines and two bibliographic databases: Google Scholar and Microsoft Academic, on the one hand, and Web of Science and Scopus, on the other. A reverse engineering methodology is employed based on the statistical analysis of Spearman’s correlation coefficients. The results indicate that the ranking algorithms used by Google Scholar and Microsoft are the two that are most heavily influenced by citations received. Indeed, citation counts are clearly the main SEO factor in these academic search engines. An unexpected finding is that, at certain points in time, Web of Science (WoS) used citations received as a key ranking factor, despite the fact that WoS support documents claim this factor does not intervene.

This article carries out a systematic review of the literature analysing the following new journalistic formats: structured journalism, immersive journalism, 360° video reports, virtual reality and augmented reality applied to journalism, newsgames and docugames. To do so, the scientific production examining these formats is first analysed to determine the academic impact of these studies, while identifying their characteristics and tracking their evolution over time. Second, the leading researchers in this field are identified and interviewed to ascertain their opinion about the future of journalism and changing trends in journalistic formats. The results show that the number of academic publications about journalistic innovation peaked in 2019, above all in the three journals – Digital Journalism, Journalism Practice and Profesional de la Información – that lead the way in this discipline. The 23 academics interviewed reported that innovation impacts primarily on four aspects of journalism: information and content; audience; methods and resources and news media companies.

In recent years, artificial intelligence has developed into a powerful tool for processing and generating human-like texts, unlocking innovative possibilities for quantitative and qualitative data analysis. Within qualitative research, artificial intelligence in general, and ChatGPT in particular, represent promising avenues to explore and examine textual transcriptions from interview data. This study is a step forward in this direction, advancing a reliable research protocol for using ChatGPT to conduct thematic analysis, which includes the following standard steps: (1) data preparation, (2) defining the analysis process, (3) chatbot interaction, (4) iterative process, (5) review and validation, and (6) analysis and interpretation. Results of the analysis revealed that ChatGPT may significantly facilitate qualitative data analysis exploration, especially during initial research stages and when dealing with extensive transcription material. Additionally, our protocol design is able to reliably identify different thematic patterns emerging from the text, although the granularity of the output may vary depending on the quality of the prompt and human intelligence interpretation. Accordingly, we conclude that despite its vast power, the ChatGPT model in its current state is unable to substitute the contextual insights and subtle metaphorical nuances associated with human qualitative analysis, interpretation and reflexivity.