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This article is a reflection on information literacy evaluation heuristics, their use in the post-secondary information literacy instructional context, and the challenges posed to them by large language models like OpenAI’s ChatGPT. Mike Caulfield’s SIFT and Jane Mandalios’ RADAR are analyzed as examples of heuristics that run into problems when used to critically assess large language models and their generated textual output. The author concludes by sharing thoughts on how he thinks information literacy instruction may be forced to change by generative artificial intelligence in the future.
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Stored only when both papers are in the corpus — 5 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.