Introducing generative AI with Markov Chains
Gendered patterns of competence in English Language Arts classrooms
Bibliographic Data
| ID | 21348293 |
|---|---|
| Authors | Daria Smyslova (0009-0008-9244-6750, North Carolina State University, corresponding author), Shiyan Jiang (0000-0003-4781-846X, North Carolina State University), Carolyn Penstein Rosé (0000-0003-1128-5155, Carnegie Mellon University), Rebecca Ellis (0000-0002-7761-468X, The Concord Consortium), Jie Chao (0000-0002-9153-3552, The Concord Consortium), Qiuqing Li (North Carolina State University), Qiankun Li (0000-0001-5121-1682, North Carolina State University) |
| Year | 2025 |
| Volume | 118 |
| Issue | 6 |
| Pages | 701-715 |
| Publication date | 2025-11-02 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | The Journal of Educational Research (JOURNAL) |
| Journal identifiers | ISSN: 0022-0671 • E-ISSN: 1940-0675 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/00220671.2025.2510409 |
| OpenAlex | W4411042732 |
| Language | EN |
| Citations received | 1 |
| References cited | 35 |
This study examined an intervention designed to foster high school students’ AI literacy through foundational text generation models. Using Markov Chains as an entry point, the study supported students’ understanding of predictive modeling and the probabilistic nature of AI-generated text. Using a mixed-methods approach, pre- and post-assessment showed significant gains in students’ self-reported competence and understanding of AI text generation, while qualitative analysis highlighted improvements in recognizing how predictive models generate text sequences. However, findings suggested that while students developed a foundational understanding, they faced challenges in extending this knowledge to more advanced AI systems. Some misconceptions also persisted, including the belief that AI-generated text is random rather than probabilistic. Also, female students tended to underestimate their competence despite slightly higher learning gains. These findings underscore the need for structured scaffolding to bridge foundational and advanced AI concepts, ensuring students develop both technical understanding and critical evaluation skills
Art · Competence (human resources) · English language · Generative grammar · Linguistics · Machine learning · Markov chain · Mathematics education · Sociology · The arts · Visual arts · Artificial Intelligence · Computer Science · Educational Games and Gamification · Online Learning and Analytics · Philosophy · Psychology · Social Psychology · Teaching and Learning Programming
A review of AI teaching and learning from 2000 to 2020
What is AI Literacy? Competencies and Design Considerations
Using chatbots to scaffold EFL students’ argumentative writing
Envisioning AI for K-12
Education in the Era of Generative Artificial Intelligence (AI)
ChatGPT for good? On opportunities and challenges of large language models for education
Lexical characteristics of young L2 English learners’ narrative writing at the start of formal instruction
The impact of Google Assistant on adolescent EFL learners’ willingness to communicate
Peer feedback or peer feedforward? Enhancing students’ argumentative peer learning processes and outcomes
ChatGPT in education
Systematic review of research on artificial intelligence applications in higher education – where are the educators
Computational Thinking in K–12
Using thematic analysis in psychology
Designing and Conducting Mixed Methods Research
| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2025 - 2025 (1) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 1 |