Due　to　the　real　problem　of　＇insufficient　basic　knowledge　in　interdisciplinary　fields＇　that　students　may　face　when　studying　interdisciplinary　courses　and　learning　interdisciplinary　knowledge,　this　study　proposes　an　＇Artificial　Intelligence　(AI)-Teaching　Assistant　(TA)　App＇　system.　The　proposed　system　combines　a　Question-and-Answer　(QA)　system,　an　expansion　mechanism,　a　cache　mechanism,　and　an　error　correction　mechanism　to　assist　students　in　their　learning　process　by　AI-based　QA　system.　Students　can　ask　＇AI-TA　App＇　questions　through　voice　and　the　App　can　quickly　provide　corresponding　answers　to　help　them　learn.　For　providing　an　efficient　＇AI-TA　App,＇　this　study　proposes　a　cache　mechanism.　A　neural　network-based　QA　system　is　built　in　the　＇AI-TA　App＇　server　and　database　to　enable　the　QA　system　to　respond　to　students＇　questions　quickly;　experimental　results　show　that　the　average　response　time　using　the　cache　mechanism　is　0.001　seconds,　while　the　average　response　time　using　ChatGPT　is　15.652　seconds,　indicating　that　the　proposed　cache　mechanism　in　this　study　can　effectively　improve　system　efficiency.　Furthermore,　this　study　also　proposes　an　expansion　mechanism.　By　calling　the　ChatGPT　APIs,　the　expansion　mechanism　expands　the　question　bank　and　improves　the　accuracy　and　robustness　of　the　QA　system;　experimental　results　show　that　the　accuracy　without　using　the　expansion　mechanism　is　25.45%,　while　the　accuracy　can　be　improved　to　99.09%　by　using　the　expansion　mechanism.　　©　2023　IEEE.