The growing availability of mobile technologies has contributed to an increase in mobile-assisted language learning in which learners can autonomously study a second language (L2) anytime or anywhere (e.g. Kukulska-Hulme, Lee & Norris, 2017; Reinders & Benson, 2017). Research investigating the effectiveness of such study for L2 learning, however, has been limited, especially regarding large-scale commercial L2 learning apps, such as Duolingo. Although one commissioned research study found favorable language learning outcomes (Vesselinov & Grego, 2012), limited independent research has reported issues related to learner persistence, motivation, and program efficacy (Lord, 2015; Nielson, 2011). The current study investigates the semester-long learning experiences and results of nine participants learning Turkish on Duolingo. The participants showed improvement on L2 measures at the end of the study, and results indicate a positive, moderate correlation between the amount of time spent on Duolingo and learning gains. In terms of perceptions of their experiences, the participants generally viewed Duolingo’s flexibility and gamification aspects positively; however, variability in motivation to study and frustration with instructional materials were also expressed.

Despite the prevalence of quantitative approaches in applied linguistics (AL) and second language acquisition (SLA) research (Gass, 2009), evidence indicates a need for improvement in analyzing and reporting SLA data (e.g., Larson-Hall & Plonsky, 2015). However, to improve quantitative research, researchers must possess the statistical knowledge necessary to conduct quality research. This study assesses AL and SLA researchers’ knowledge of key statistical concepts on a statistical knowledge test. One hundred and ninety-eight AL and SLA researchers from North America and Europe responded to 26 discipline-specific questions designed to measure participants’ ability to (a) understand basic statistical concepts and procedures, (b) interpret statistical analyses, and (c) critically evaluate statistical information. Results indicate that participants generally understood basic descriptive statistics, but performance on items requiring more advanced statistical knowledge was lower. Quantitative research orientation, number of statistics courses taken, and frequent use of statistics textbooks had positive influences on researchers’ statistical knowledge.

Progress made in Natural Language Processing (NLP) and Artificial Intelligence (AI) in recent years has resulted in these tools becoming more accessible for individuals who lack professional training. Of particular note are large language models, such as OpenAI's GPT‐3.5. Discussions of utilizing AI for language education usually focus on the impact the technology will have on students and teachers. Less frequently the center of attention is how generative AI tools can empower researchers. The purpose of this paper is to raise awareness by demonstrating and discussing examples of how OpenAI's chatbot, ChatGPT, can be leveraged as a tool for language education researchers. After briefly introducing the use of AI generative tools in the field, this paper demonstrates how a researcher, without any understanding of NLP or AI, may use ChatGPT to assist with research through multiple means, including approaches to its use for compiling and summarizing information, and as a research assistant throughout multiple steps of research. This is followed by a discussion of potential ethical concerns of using AI for research in the field. We conclude by issuing a call for further work examining how researchers can harness the potential of this technology in ethical ways.

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