Teaching
How Dr. Hammonds teaches, and what to expect.
Dr. Hammonds has been teaching since 2010 — first in Texas secondary schools, then through graduate school, and now at UT Dallas. The page below answers the questions students most often ask before they sign up for one of his courses.
Approach
What does a course with Dr. Hammonds look like?
His courses are built around close reading — of film, of comics, of journalism, of academic texts. Students watch and read carefully, then ask why a maker made the choices they made and what those choices invite an audience to feel, believe, or do.
Writing is central. Every course requires significant written work, because writing is the discipline that forces a reader to defend their reading. Dr. Hammonds would rather see one careful paragraph than five careless ones, and he grades with that preference in mind.
Discussion is the second engine. Small-group and full-class conversations run regularly, and students are expected to arrive with questions, not just answers. Strong participation routinely shapes a final grade for the better.
Popular culture sits at the center of much of his undergraduate teaching. Comics, superhero films, podcasts, and the work fan communities do online are not lighter material than canonical texts. They are simply more recent, and often closer to the political and social questions that students are already thinking about.
AI policy
On the use of generative AI in coursework.
[Placeholder policy. To be replaced with Dr. Hammonds's verbatim text once provided.]
Generative AI tools — large language models, image generators, transcription assistants — have become part of the working environment students will enter after graduation. A useful classroom policy needs to engage with them rather than wish them away.
The working principle distinguishes between two kinds of use. The first is process work: brainstorming, outlining, summarizing source material, or drafting a list of questions to ask of a text. The second is product work: producing the artifact that a course is intended to assess. The first is generally permitted with appropriate citation. The second is generally not.
The specifics are detailed in each course syllabus, including expectations for citing AI assistance, which assignments are AI-restricted, and how violations are handled.
Questions
What students usually ask.
These are the questions I hear most often during advising sessions and the first week of class. If yours is not listed, send a message and I will answer it directly.
How difficult are these courses?
The honest answer is that the difficulty depends on what kind of difficult is meant. The courses are not memorization-heavy. There are few timed tests. The work asks students to read carefully, watch carefully, and write clearly about what they saw. That is harder than it sounds, and it gets easier with practice.
Students who put in steady effort across the term tend to do well. Students who try to compress the work into the final two weeks tend not to.
What is Dr. Hammonds’s AI policy?
[Placeholder — to be replaced with Dr. Hammonds's verbatim policy.]
Generative AI tools are part of the working environment students will enter after graduation, and a careful policy needs to reflect that. The current draft distinguishes between using AI for brainstorming and process work versus using it to produce the final artifact a course is meant to assess. Detailed guidance lives in each course syllabus.
How much homework should students expect?
A reasonable working estimate is six to nine hours per week outside of class, including reading, viewing assigned media, and writing. Some weeks run lighter; the weeks leading up to major papers run heavier.
A full schedule is posted on the first day of the semester so students can plan around exams, work, and other obligations.
Is a background in film or media studies required?
Are these courses discussion-heavy or lecture-heavy?
Recognition
Outstanding Teacher Award, 2026.
Recognized by the School of Interdisciplinary Studies at UT Dallas. Previous recognition includes the Outstanding Graduate Student Teaching Award (2022, University of Oklahoma) and Outstanding Teaching by a Graduate Student (2016, University of North Texas).