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I am searching for a new job. A job that I can combine with my studies and which can provide me with enough to allow my shoe-box-sized apartment. But to get there, one often needs to write long motivational letters to various organisations and go through various potential job postings. However, the new age offers many opportunities to write motivational letters automatically and adapt to each and every company.  

In this search, I tested three separate AI-powered websites, ChatGPT, Kickresume, LazyApply and Rezi.

Kickresume, LazyApply and Rezi all provide a free trial of the algorithm that formulates extensive motivation letters. What is more, they all provide an easy User experience. The latter three websites also provide the user with prompts, like ‘’paste the job description’’ and ‘’paste your CV’’ which can provide a great deal of intertwined attention to one’s abilities and the required skills. The given prompts can also be skipped or occasionally modified if deemed to be unnecessary. Therefore, a complementary document can be readily made if one has a CV.

Regarding more mainstream and wide-use AI-language models like ChatGPT, one needs to insert a significant number of self-created prompts to create even a slightly similar quality document. It can be a helpful tool for people with more background knowledge of HR. For others, it can also complicate the creative process even more since Farrohina et al. (2023) find that AI language tools, if not used right, can significantly hinder one’s productivity and creativity.

Baert and Verhaest (2019) also emphasize that overqualification in the application process does not lower one’s chances of receiving the job and even increases the chances of employment for temporary jobs. Therefore, additional effort can not be of harm.  

Overall, all platforms provide similar-level content and are an excellent tool to create a personalized motivation letter. Sadly, the lack of layout options persists but can be easily tackled, by the use of other platforms.

Last but not least, the AI language models are built upon similar documents; therefore, the originality can only reach as far. Hence, the generated letters can come out to be too generic if applied to highly sought-after positions. Therefore, as helpful as these websites can be, they cannot replace well-thought-out and personal material.

Baert, S., & Verhaest, D. (2019). Unemployment or overeducation: which is a worse signal to employers? De Economist, 167(1), 1-21.

Farrokhnia, M., Banihashem, S. K., Noroozi, O., & Wals, A. (2023). A SWOT analysis of ChatGPT: Implications for educational practice and research. Innovations in Education and Teaching International, 1-15.

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Almost in no time, AI-powered large language models (LLMs) such as ChatGPT, Bing AI Chat, Google Bard AI, etc., have gained popularity among the mainstream part of society. However, I have noticed increased social media attention, specifically among Latvian language speakers, about the lack of applicability and, oftentimes, even comedic outputs these language models create.

I test this observation by typing ‘write a poem’ in multiple languages in the dialogue interface of ChatGPT. English, Russian, French, Arabic, Hindi, Dutch, Latvian, Estonian and Lithuanian. Interestingly, although ChatGPT can produce, to some extent, coherent text in all prior languages, the latter three, i.e., the Baltic countries, excel with incoherent meanings and even grammar and style inconsistencies. Bang et al. (2023) argue that these are low-resource languages, i.e., languages with relatively few speakers. Not surprisingly, Latvian is spoken by 1,5 million native inhabitants (Latvian Presidency, 2023), and the AI model has not received the necessary data input to produce grammatically or style-wise coherent sentences (see picture).

So, how can this be an issue?

In Baltic countries, approximately 95% of individuals speak at least two languages (Latvian Presidency, 2015). The second language most often is Russian or English, i.e., high-resource languages.

This is a worry, as many native speakers might instead stick to high-resource languages while browsing or creating content. This, in return, reciprocates the poor usability of low-resource languages and exacerbates language polarization. The low-resource languages are, therefore, at risk unless new measures are implemented to better the LLM training, e.g., training AI with ‘small data’ as suggested by Ogueji, Zhu, Lin (2021), or feeding AI with new data resources.

Of course, the future of the language is not as one-dimensional and depends on many factors, but at times of language globalization, the mainstream AI tools have helped no further!