AI Scholar-Bots Created from Published Works Perform Academic Functions at Senior Lecturer Level
Scholarly reasoning frameworks were derived from the works of two distinguished scholars in the humanities and social sciences, and these were transformed into structured guidelines for a large language model. The resulting scholar-bots underwent testing in essential academic roles such as doctoral supervision, peer review, lecturing, and panel discussions. A comprehensive extraction process involving eight layers and a nine-module skill framework based on closed-corpus analysis was utilized. Three senior academics conducted expert evaluations, providing reports and syntheses at the appointment level. All assessments deemed the AI outputs to meet benchmark standards, with recommendations positioning both scholar-bots at or above the Senior Lecturer tier in the Australian university framework. This research illustrates the potential of published scholarship to inform AI systems capable of executing expert academic tasks.
Key facts
- Scholarly reasoning systems were extracted from two prominent humanities and social science scholars' published corpora
- The systems were converted into structured inference-time constraints for a large language model
- Scholar-bots were tested across doctoral supervision, peer review, lecturing, and panel-style academic exchange
- An eight-layer extraction method and nine-module skill architecture were used in the distillation pipeline
- Three senior academics conducted expert assessment through reports and appointment-level syntheses
- All review and supervision reports judged the AI outputs as benchmark-attaining
- Appointment-level recommendations placed both bots at or above Senior Lecturer level in Australian universities
- The research was published on arXiv with identifier 2604.16116v1
Entities
Institutions
- arXiv
- Australian universities
Locations
- Australia