Generalized　zero-shot　learning　(GZSL)　aims　to　recognize　samples　whose　categories　may　not　have　been　seen　at　training.　Standard　GZSL　cannot　handle　dynamic　addition　of　new　seen　and　unseen　classes.　In　order　to　address　this　limitation,　some　recent　attempts　have　been　made　to　develop　continual　GZSL　methods.　However,　these　methods　require　end-users　to　continuously　collect　and　annotate　numerous　seen　class　samples,　which　is　unrealistic　and　hampers　the　applicability　in　the　real-world.　Accordingly,　in　this　paper,　we　propose　a　more　practical　and　challenging　setting　named　Generalized　Zero-Shot　Class　Incremental　Learning　(CI-GZSL).　Our　setting　aims　to　incrementally　learn　unseen　classes　without　any　training　samples,　while　recognizing　all　classes　previously　encountered.　We　further　propose　a　bi-level　meta-learning　based　method　called　MetaZSCIL　to　directly　optimize　the　network　to　learn　how　to　incrementally　learn.　Specifically,　we　sample　sequential　tasks　from　seen　classes　during　the　offline　training　to　simulate　the　incremental　learning　process.　For　each　task,　the　model　is　learned　using　a　meta-objective　such　that　it　is　capable　to　perform　fast　adaptation　without　forgetting.　Note　that　our　optimization　can　be　flexibly　equipped　with　most　existing　generative　methods　to　tackle　CI-GZSL.　This　work　introduces　a　feature　generative　framework　that　leverages　visual　feature　distribution　alignment　to　produce　replayed　samples　of　previously　seen　classes　to　reduce　catastrophic　forgetting.　Extensive　experiments　conducted　on　five　widely　used　benchmarks　demonstrate　the　superiority　of　our　proposed　method.　Copyright　©　2023,　Association　for　the　Advancement　of　Artificial　Intelligence　(www.aaai.org).　All　rights　reserved.