A collection of useful works on AI and writing, Higher education etc.
Seminal works on teacher-provided feedback in higher education
| Hyland, K., & Hyland, F. (2006). Feedback on second language students' writing. Language Teaching, 39(2), 83-101. | A seminal article discussing feedback on L2 student writing, including a comprehensive review of different feedback types, strategies, and their effectiveness. |
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| Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81-112. | Influential article presenting a model of feedback that supports learning, applicable to written assignments. |
| Nicol, D. J., & Macfarlane-Dick, D. (2006). Formative assessment and self-regulated learning: a model and seven principles of good feedback practice. Studies in Higher Education, 31(2), 199-218. | An essential work that discusses the principles of good feedback practice, with a focus on formative assessment. |
| Shute, V. J. (2008). Focus on formative feedback. Review of Educational Research, 78(1), 153-189. | Provides a comprehensive review of formative feedback in education, emphasizing the importance of specific and timely feedback. |
| Sadler, D. R. (1989). Formative assessment and the design of instructional systems. Instructional Science, 18(2), 119-144. | A classic article discussing how formative assessment, including feedback, can be integrated into instructional design. |
Classroom-based examples of teachers providing writing feedback
| Lee, I. (2008). Student reactions to teacher feedback in two Hong Kong secondary classrooms. Journal of Second Language Writing, 17(3), 144-164. | This study explores student reactions to teacher feedback in two Hong Kong classrooms, offering a nuanced understanding of classroom-based feedback. |
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| Bitchener, J., & Knoch, U. (2010). The contribution of written corrective feedback to language development: A ten-month investigation. Applied Linguistics, 31(2), 193-214. | A longitudinal study investigating how written feedback can contribute to the development of second language students in a classroom setting. |
| Goldstein, L. M. (2004). Questions and answers about teacher written commentary and student revision: teachers and students working together. Journal of Second Language Writing, 13(1), 63-80. | Investigates how teacher-written commentary affects student revision practices in classroom settings. |
| Ferris, D. R. (2006). Does error feedback help student writers? New evidence on the short- and long-term effects of written error correction. In Feedback in second language writing: Context and issues (pp. 81-104). Cambridge University Press. | Evaluates the effects of error correction, a common form of feedback in writing classes. |
| Lyster, R., & Ranta, L. (1997). Corrective feedback and learner uptake. Studies in second language acquisition, 19(1), 37-66. | Classic work exploring corrective feedback in second language acquisition within the classroom context. |
Teachers providing writing feedback in online teaching and learning
| Winstone, N., & Carless, D. (2020). Designing effective feedback processes in higher education: A learning-focused approach. Routledge. | This book offers principles and strategies for effective feedback that are applicable in online contexts. |
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| Gibbs, G., & Simpson, C. (2004). Conditions under which assessment supports students’ learning. Learning and Teaching in Higher Education, 1(1), 3-31. | Offers principles on how assessment, including feedback, can support student learning in online teaching contexts. |
| Ertmer, P. A., Richardson, J. C., Belland, B., Camin, D., Connolly, P., Coulthard, G., Lei, K., & Mong, C. (2007). Using peer feedback to enhance the quality of student online postings: An exploratory study. Journal of Computer-Mediated Communication, 12(2), 412-433. | This study investigates the use of peer feedback in online courses and how it can enhance the quality of student postings. |
| Liu, N. F., & Carless, D. (2006). Peer feedback: the learning element of peer assessment. Teaching in Higher education, 11(3), 279-290. | Discusses peer feedback in online learning environments, focusing on its potential for promoting learning. |
| Guasch, T., Espasa, A., Alvarez, I. M., & Kirschner, P. A. (2013). Effects of feedback on collaborative writing in an online learning environment. Distance Education, 34(3), 324-338. | This study explores the effects of feedback in an online collaborative writing environment, offering insights into how feedback functions in online learning. |
Use of AI in higher education
| Popenici, S. A., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12(1), 1-13. | Investigates the potential impacts of AI on teaching and learning in higher education, offering a broad overview of AI application in this context. |
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| Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence Unleashed: An argument for AI in Education. Pearson. | An argumentative piece advocating for the use of AI in education, detailing potential advantages and applications. |
| Blikstein, P., & Paiva, A. (2019). The new promises of educational technology. AI & Society, 34(4), 973-978. | Discusses the potential of new AI technologies to revolutionize education, highlighting both potential benefits and challenges. |
| Blikstein, P., & Worsley, M. (2016). Multimodal learning analytics and education data mining: using computational technologies to measure complex learning tasks. Journal of Learning Analytics, 3(2), 220-238. | This paper explores the use of AI in analyzing complex learning tasks in education, offering insights into multimodal learning analytics. |
| Weller, M. (2020). 25 Years of Ed Tech. Athabasca University Press. | A comprehensive review of educational technologies, including AI, used over the past 25 years in higher education. |
| Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural networks, 61, 85-117. | An overview of deep learning, a subset of AI, and its applications in various domains, including education. |
Use of AI in literary and rhetorical subjects in higher education
| Bektik, D. (2017). Can Machines Read Our Minds? AI & Society, 32(2), 205-217. | Explores the possibilities of AI for analyzing student writing and providing feedback in higher education contexts. |
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| Rosé, C. P., Wang, Y. C., Cui, Y., Arguello, J., Stegmann, K., Weinberger, A., & Fischer, F. (2008). Analyzing collaborative learning processes automatically: Exploiting the advances of computational linguistics in computer-supported collaborative learning. International Journal of Computer-Supported Collaborative Learning, 3(3), 237-271. | Discusses the use of AI for analyzing collaborative learning processes, a relevant concept for literary and rhetorical subjects where collaboration is often key. |
| Sherin, B. (2013). A computational study of commonsense science: An exploration in the automated analysis of clinical interview data. Journal of the Learning Sciences, 22(4), 600-638. | A study on the use of AI in analyzing student's common science understanding, which could be extrapolated to other disciplines such as literary and rhetorical subjects. |
| Sun, Y., & Yeung, D. Y. (2012). To robo-grade or not to robo-grade: A comparison study of a robo-grading system and human graders. In Proceedings of the National Conference on Artificial Intelligence (Vol. 1, pp. 25-30). | This paper presents a comparison study of an AI-based grading system and human graders for evaluating student essays in literary courses. |
| Clarke, S. G., & Nelson, M. E. (2011). Robot writers: A study of the formulaic construction of computer-generated writing. Journal of Business and Technical Communication, 25(2), 211-235. | This study investigates the formulaic construction of computer-generated writing, relevant to the application of AI in literary and rhetorical subjects. |
| Thies, R. (2018). The rise of machines: An overview of artificial intelligence in composition pedagogy. Computers and Composition, 49, 39-52. | This article provides an overview of the use of AI in composition pedagogy, highlighting both benefits and challenges. |
Student perceptions of AI use in higher education
| Hew, K. F., & Brush, T. (2007). Integrating technology into K-12 teaching and learning: current knowledge gaps and recommendations for future research. Educational Technology Research and Development, 55(3), 223-252. | While not exclusive to higher education, this research sheds light on student attitudes and perceptions toward technology and AI use in education. |
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| Wang, Y., & Baker, R. (2018). Grit and intention to learn in online learning environments. Journal of Educational Computing Research, 56(4), 506-518. | This research explores how students perceive online learning environments driven by AI and their learning intentions. |
| Li, H., Xiong, Y., Zang, X., Kornhaber, M. L., Lyu, Y., Chung, K. S., & Suen, H. K. (2016). Peer assessment in the digital age: A meta-analysis comparing peer and teacher ratings. Assessment & Evaluation in Higher Education, 41(2), 245-264. | Li et al. (2016) conducted a meta-analysis on peer and teacher ratings in the context of digital peer assessment. They found that while there is a high degree of correlation between peer and teacher assessments, peer ratings tend to be slightly higher, suggesting the need for careful design and implementation of peer assessment in digital environments. |
| Gikas, J., & Grant, M. M. (2013). Mobile computing devices in higher education: Student perspectives on learning with cellphones, smartphones & social media. The Internet and Higher Education, 19, 18-26. | This study explores student perceptions of learning with mobile computing devices, including AI-based applications, in higher education. |
| Hew, K. F., & Brush, T. (2007). Integrating technology into K-12 teaching and learning: current knowledge gaps and recommendations for future research. Educational Technology Research and Development, 55(3), 223-252. | This research provides a comprehensive review of the current knowledge gaps in integrating technology, including AI, into teaching and learning, with insights on student perceptions. |
| Händel, M., Stephan, M., Gläser-Zikuda, M., Kopp, B., Bedenlier, S., & Ziegler, A. (2020). Digital readiness and its effects on higher education students’ socio-emotional perceptions in the context of the COVID-19 pandemic. Journal of Research on Technology in Education, 1-16. | This paper investigates student perceptions of digital readiness, including the use of AI, in the context of pandemic-induced remote learning. |
Teacher Perceptions of AI use in Higher Education
| Ertmer, P. A. (2005). Teacher pedagogical beliefs: The final frontier in our quest for technology integration? Educational Technology Research and Development, 53(4), 25-39. | This study discusses teachers' pedagogical beliefs, including their perceptions of AI, as a significant factor in technology integration. |
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| Ertmer, P. A., & Ottenbreit-Leftwich, A. T. (2010). Teacher technology change: How knowledge, confidence, beliefs, and culture intersect. Journal of research on Technology in Education, 42(3), 255-284. | This research explores how teacher knowledge, confidence, beliefs, and culture intersect when it comes to technology change, including AI. |
| Anthony, B., & Clark, L. (2014). Investigation of the New Technologies Teachers Use to Improve Mathematics Instruction. Journal of Educational Technology, 11(1), 25-41. | This paper investigates the new technologies, including AI, teachers use to improve instruction, with insights on teacher perceptions. |
| Fidalgo-Blanco, Á., Sein-Echaluce, M. L., García-Peñalvo, F. J., & Conde, M. Á. (2017). Using Learning Analytics to Improve Teamwork Assessment. Computers in Human Behavior, 72, 492-509. | This study delves into the use of learning analytics in assessing teamwork, providing an insight into teachers' perceptions of AI's role in evaluating collaborative work in higher education. |
| Herodotou, C., Rienties, B., Hlosta, M., Boroowa, A., Mangafa, C., & Zdrahal, Z. (2020). The scalable implementation of predictive learning analytics at a distance learning university: Insights from a longitudinal case study. The Internet and Higher Education, 45, 100725. | This longitudinal case study examines the scalable implementation of predictive learning analytics in a distance learning setting. It offers useful perspectives on teacher perceptions of AI in supporting student learning and retention. |
Writing Feedback Writing Feedback Using Generative AI
Dai, W., Lin, J., Jin, F., Li, T., Tsai, Y., Gasevic, D., & Chen, G. (2023, April 13). Can Large Language Models Provide Feedback to Students? A Case Study on ChatGPT. https://doi.org/10.35542/osf.io/hcgzj
Jones, K., Nurse, J. R., & Li, S. (2022, May). Are you Robert or Roberta? deceiving online authorship attribution models using neural text generators. In Proceedings of the International AAAI Conference on Web and Social Media (16). 429-440.
Kung, T.H., Cheatham, M., ChatGPT, Medenilla, A., Sillos, C., De Leon, D., Elepaño, C, Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., Tseng, V. (2022). Performance of ChatGPT on USMLE: Potential for AI-Assisted Medical Education Using Large Language Models. PLOS Digit Health 2(2). https://doi.org/10.1371/journal.pdig.0000198 .
Murphy-Kelly, A. (January 26, 2023). ChatGPT passes exams from law and business schools. Cable News Network. https://edition.cnn.com/2023/01/26/tech/chatgpt-passes-exams/index.html