We find ourselves at a pivotal moment where the role of generative AI and other technological tools is reshaping the way we teach and learn languages. The question that lies before us is not whether these innovations will shape the future, but rather how we, as educators, will harness their potential to create meaningful and effective language instruction.
The title of our conference, No Fate: The Future is Not Set, underscores our belief that the future is not predetermined. We hope that our conference will serve both as a platform for collaboration and a catalyst for change as it is via the collective effort of educators, researchers, and innovators that the trajectory of language teaching and learning will be determined. By fostering collaboration, sharing insights, and pushing the boundaries of what is possible, we can shape the future of language education.
Since the field of teaching Chinese as a foreign language (CFL) is increasingly transitioning away from requiring students to handwrite characters to recognize characters, a question that naturally arises is the relative efficacy of the two methods in terms of 1) speed of acquisition, 2) memorization of meaning, and 3) learning of pronunciation. Further, this study explored the impacts of visual and spatial working memory on L2 acquisition of characters. A total of 60 low-frequency characters were selected as stimuli, among which 20 had 3 strokes, 20 had 5 strokes, and 20 had 7 strokes. Sixty pronunciations were assigned to each character and read aloud by a male voice with the first tone. These characters were evenly distributed into two lists with counter-balancing performed on consonants, vowels, structures, and stroke numbers to make the two lists equally easy to learn. All participants learned half of the characters using the handwriting method and the other half using the recognition method. Participants were 36 L2 learners of Chinese at the intermediate-low level of proficiency. The 60 characters were put into 6 blocks, and each block had 10 characters that the participants studied for 10 minutes. After that, they did a lexical decision task, a naming task, and a meaning-recall task. Results showed that 1) the recognition method was 23% faster in learning characters than the handwriting method; 2) both methods performed equally well with a 92% accuracy rate in the lexical decision task; 3) spatial working memory was positively correlated with lexical decision accuracy and speed; and 4) the recognition method was 10% better at remembering pronunciations (75% vs 65%) and 3% better at meaning-recall (84% vs 81%) than the handwriting method (all ps＜.05), thus lending empirical support for the current trend in CFL to replace the requirement of character-handwriting with character-typing, given that the recognition method mirrors the essential skill set required for character-typing.
