This contribution examines the digital revolution in lexicography from the perspective of the dictionary user. We begin with an observation that in the information age the status of the dictionary is changing, and so are patterns of user behaviour, with general internet search engines encroaching on the grounds traditionally reserved for lexicographic queries. Clearly, we need to know more about user behaviour in the digital environment, and for this we need to harness user research, to find out how the increasingly flexible and adaptive lexical reference tools of the future need to behave to best accommodate user needs. We summarize the existing findings and show in what ways digital dictionaries are already able to serve users better than their paper predecessors. The challenge to produce efficient and effective dictionaries is best seen in the context of dictionary users’ reference skills, which now tend to overlap with digital literacy. We conclude with a possible vision of the future.

We present the KELLY project and its work on developing monolingual and bilingual word lists for language learning, using corpus methods, for nine languages and thirty-six language pairs. We describe the method and discuss the many challenges encountered. We have loaded the data into an online database to make it accessible for anyone to explore and we present our own first explorations of it. The focus of the paper is thus twofold, covering pedagogical and methodological aspects of the lists’ construction, and linguistic aspects of the by-product of the project, the KELLY database.

Corpora have given rise to a wide range of lexicographic resources aimed at helping novice users of academic English with their writing. This includes academic vocabulary lists, a variety of textbooks, and even a bespoke academic English dictionary. However, writers may not be familiar with these resources or may not be sufficiently aware of the lexical shortcomings of their emerging texts to trigger the need to use such help in the first place. Moreover, writers who have to stop writing to look up a word can be distracted from getting their ideas down on paper. The ColloCaid project (www.collocaid.uk) aims to address these problems by integrating information on collocation with text editors. In this paper, we share the research underpinning the initial development of ColloCaid by detailing the rationale of (1) the lexicographic database we are compiling to support the collocation needs of novice users of English for Academic Purposes (EAP) and (2) the preliminary visualisation decisions taken to present information on collocation to EAP users without disrupting their writing. We conclude the paper by outlining the next steps in the research.

Creating high-quality dictionary entries has been a meticulous and time-consuming process, but current developments in Artificial Intelligence (AI) could help to speed up this process. This study explores the use of ChatGPT to create COBUILD-style English monolingual entries and evaluates its ability to generate accurate and high-quality sense definitions and example sentences on a sample of fifteen verbs of communication. AI-generated entries are produced using ChatGPT Plus, and human experts evaluate them alongside COBUILD entries in a blinded randomized process. The quality of sense definitions, example sentences, and overall entries are assessed using a five-point scale. The results indicate that AI-generated definitions are comparable to those created by human lexicographers, while AI-generated examples and overall entries receive lower ratings. The study also explores the possibility of fine-tuning AI models based on expert feedback to enhance their performance.

The present study revisits the impact of the introduction of AI tools on dictionary use, aiming to further explore which lexical resources English majors tend to use for a broader range of language-related tasks. From April to June 2025, 179 students from two large public Polish universities, University of Warmia and Mazury in Olsztyn and University of Gdańsk, were asked to complete 30 language-based activities and report what specific language tools they had used for each task. Available data suggest that Polish students of English rely on AI chatbots for a variety of linguistic purposes, including writing assistance, sentence translation and formation, sense selection, collocation identification, specialized (legal) translation, and phrasal verb consultation. In their efforts to locate appropriate synonyms and antonyms, students tend to use diverse tools, including AI tools, but also thesauri, monolingual learners’ dictionaries, and online search engines like Google. AI chatbots and monolingual learners’ dictionaries appear to be the preferred sources for grammar support, spelling verification, and acquiring information about idioms. For pronunciation support and defining, students favour monolingual learners’ dictionaries, whereas bilingual dictionaries are their top choice for direct word-for-word translation. When it comes to devices used to access lexical resources, our findings confirm the growing preference for the smartphone.

This paper examines the implications of AI and machine translation on traditional lexicography, using three canonical scenarios for dictionary use: text reception, text production, and text translation as test cases. With the advent of high-capacity, AI-driven language models such as OpenAI’s GPT-3 and GPT-4, and the efficacy of machine translation, the utility of conventional dictionaries comes under question. Despite these advancements, the study finds that lexicography remains relevant, especially for less-documented languages where AI falls short, but human lexicographers excel in data-sparse environments. It argues for the importance of lexicography in promoting linguistic diversity and maintaining the integrity of lesser-known languages. Moreover, as AI technologies progress, they present opportunities for lexicographers to expand their methodology and embrace interdisciplinarity. The role of lexicographers is likely to shift towards guiding and refining increasingly automated tools, ensuring ethical linguistic data use, and counteracting AI biases.