Do students read course readings? Mostly NOT! Generative AI to the rescue

Course coordinators add readings to their course ideally to support, extend, and connect student thinking about the content explicitly delivered in the face-to-face and the online blended components. There is a widespread belief the practice is highly useful (Miller and Meridian, 2020). However, the ideal and the reality seem to be at large odds with each other. Deale and Lee (2021) cite Berry (2010) in stating that only 20% of students are likely to complete course readings. Whilst limited in its scope, the figures in the study anecdotally ring true for many course coordinators in higher education.

The issues

1. Lack of reading skill – Brost and Bradley (2006) suggest that students don’t read what is assigned because they don’t have the skills to do so. The chosen texts are of course written by academics for an audience of academic peers. The language is highly sophisticated, and at times, the writing is dense. It is not that we don’t want students to be engaged in such complexity, but it is just that the academic and indeed the intended audience’s ability to comprehend the reading is far superior to the student.
2. Verbosity – It may be that some readings, whilst containing key information, also contain enormous amounts of superfluous information. This is particularly the case in older texts. Brost and Bradley’s solution is for course coordinators to be more explicit in the intention of the reading, and also to break readings up into more manageable sections.
3. Time – In a study by Smale (2020), students cited the need to prioritise what gets read and what doesn’t. Often, the perceived importance of the course is a factor in which course readings get done.
4. Poor design – If a weekly module presents five or six readings one after the other, then it is likely that not all of these will be read by the student.
5. It’s the same stuff – If the student perceives that effectively all of the readings are the same, just different ways of expressing the content, then they will make an executive decision about which one to read, and you could bet that it would be the one with the less number of words.
6. Explicit relevance – Even the diligent student will quickly learn to stop investing time into the module/topic readings if there is no reference made to them in the lecture or the tutorial. In this vein, the readings are also likely to be of little value if the student hasn’t taken notes and actively engaged in making connections between the readings and course content, because the reading’s content will be at the mercy of the forgetting curve.
7. Students won’t believe you – Students who don’t prioritise readings achieve worse outcomes in exams (Kerr and Frase, 2017), and yet students simply do not believe it (Murden & Gillespie, 1997). They think the lecture and tutorial are enough.

Using Generative AI to summarise course readings

Readings may be more beneficial to students if their inclusion in a course module is more carefully considered. Readings with lots of superfluous text could be summarised more efficiently using AI. This may be something you actively get the students to do using ChatGPT or ChatPDF, or it could be done by yourself and added to the module page alongside the actual reading. In this way, you still provide students with the opportunity to explore the reading in-depth, whilst being pragmatic about the reality of the decision the majority will take.

ChatPDF allows a student to upload a PDF reading to its site and then prompts the student to ask it questions about the reading. The types of questions the student would typically ask would undoubtedly be drawn from the theme and tone of the weekly topic. If you instruct students to use the tool in a way that helps them to see how the reading is connected to the topic, then the reading immediately becomes more meaningful. Like with ChatGPT, it is the quality of the questions that will determine the quality of the output from the tool.

Still offering challenge

Of course, any reading you include in the weekly content will be but one piece of the topic puzzle, and this is where there is plenty of opportunity for differentiation. Some students will see the reading summary and use it as a reinforcement of the broader topic, but more astute students will use AI to determine how the reading complements other aspects of the topic.

‌References

Brost, B., & Bradley, K. (2012). Student Compliance with Assigned Reading: a case study. Journal of the Scholarship of Teaching and Learning, 6(2), 101–111. Retrieved from https://scholarworks.iu.edu/journals/index.php/josotl/article/view/1667

Deale, C.S. and Lee, S.H. (Jenna) (2021). To Read or Not to Read? Exploring the Reading Habits of Hospitality Management Students. Journal of Hospitality & Tourism Education, pp.1–12. doi:https://doi.org/10.1080/10963758.2020.1868317.

Kerr, M.M. and Frese, K.M. (2016). Reading to Learn or Learning to Read? Engaging College Students in Course Readings. College Teaching, 65(1), pp.28–31. doi:10.1080/87567555.2016.1222577.

Miller, K., & Merdian, H. (2020). “It’s not a waste of time!” Academics’ views on the role and function of academic reading: A thematic analysis. Journal of University Teaching & Learning Practice, 17(2). https://doi.org/10.53761/1.17.2.3

Murden, T., & Gillespie, C. S. (1997). The role of textbooks and reading in content area classrooms: What are teachers and students saying. In W. Linek & E. Sturtevant (Eds.), Exploring literacy (pp. 85–96). College Reading Association. [Google Scholar]

Sites.bu.edu. (n.d.). ‘It’s a lot to take in’ – Undergraduate Experiences with Assigned Reading | IMPACT. [online] Available at: https://sites.bu.edu/impact/previous-issues/impact-winter-2020/its-a-lot-to-take-in/.

I’m Paul Moss. I’m a learning designer at the University of Adelaide. Follow me on Twitter @edmerger