The increasing use of English as a Lingua Franca (ELF) worldwide has brought attention to tools that can assist English as a Foreign Language (EFL) learners in their journey to fluency. Much research has shown that EFL learners often do not have sufficient latitude to output at a satisfactory level when writing in a second language. In addition, cognitive (working memory) resources are spent on low-level writing tasks (word production, translation) at the expense of time being allocated to higher-level writing tasks such as organization and revision. The researcher's laboratory developed an AI-based web application called “AI KAKU” to assist EFL learners in reducing the cognitive barriers they face when producing written text in English. While there has been much research and discussion on Automated Writing Evaluation (AWE) technologies or older technologies such as spell check and grammar check, few studies have attempted to use AI-based tools as learning instruments outside assessments. This study recruited adult EFL participants in a counter-balanced experiment to evaluate the potential impact of AI KAKU on student writing. Preliminary results indicate that this is a potentially useful tool for English language learners who need more structured assistance than traditional word processors.

This Research to Practice Full Paper investigates second language learners' writing output using an online next-word prediction writing tool after exposure to training and metacognitive prompts to improve their critical thinking. Engineering graduates' writing skills are often deemed lacking by industry standards; this can be even more challenging for English as a foreign language (EFL) learners. This study employs a randomized control trial with university-level participants using an internally developed writing aid with next-word prediction, reverse translation support, and metacognitive prompts. EFL participants were given question prompts in the TOEFL iBT independent writing task style, and the outputs were assessed (machine and human) using several measures for writing quality. All participants were shown short explanatory videos for TOEFL writing advice and metacognition training. The treatment group, exposed to the next-word prediction writing aid and metacognitive prompts, performed better than the control group even though both received the same training and writing opportunities. This study indicates there is value in providing writing support and metacognitive thinking practice to improve writing skills and, ultimately, writing output quality. This study's implications can be applied not only to EFL learners but also to engineering-related fields using English as a lingua franca.

The climate in urban centers can differ significantly from the immediate surrounding areas; this can pose health risks to the elderly who have spent much of their lives in urban centers and then move to more rural areas for retirement. Therefore, there is a need to develop educational software applications for the elderly that needs to account for their life experiences aside from physiological restrictions. This research created a connected wireframe for an educational assistant to make the elderly aware of the climate differences between their urban and rural residences. To address the difficulty of recruiting vulnerable subjects, especially during the COVID-19 pandemic, we evaluated our wireframe using a combination of heuristic evaluation and a variation of the Delphi process, an expert consensus-building tool typically used for market forecasting.

As practitioners introduce new educational technologies into their classrooms, the potential for unintended outcomes from their use might arise. One such potential negative artifact is an increase in the achievement gaps between learners, where high performers tend to benefit more from newly introduced educational technologies than their peers. This phenomenon is commonly referred to as the Matthew effect. In this study, we leverage natural language processing (NLP) based transformers to introduce English language support to English as a Foreign Language (EFL) learners while they are in the writing process. A web-based application was created that uses next-word prediction and automatic reverse translation to help EFL participants in their writing.Adult English language learners from professional development language schools participated in a counterbalanced repeated measures study. To understand the presence of the Matthew effect, learners were grouped based on their self-reported EIKEN scores. Their performance according to two writing factors as well as their perceived cognitive load while using the tool were measured to establish which groups benefit the most from using the tool. This research sets the stage for understanding how emerging tools cansupport learning without exacerbating Matthew effects. These effects should beconsidered in both the development and application of educational technology.

Multiple studies have hinted at the positive effects of educational games on learner engagement. However, some games, if they exist, may not be readily usable for some lessons even though the same topics are covered. In this workshop, the participants will be introduced to Twine, an open-source development environment for nonlinear stories that requires little to no coding. Tips and tricks to making engaging stories that target desired learning outcomes derived from existing research will be shared. The participants will have the opportunity to apply these insights through a group activity where they create their stories for a lesson. They will then individually create games for their stories and share their works to the rest of the group. An online community will be created after the workshop for the participants to continue exchanging ideas or creations afterwards.