<p>Episodic memory is a type of long-term memory that encodes and retrieves personal experiences associated with their context. Previous episodic memory studies showed that the context or preexisting knowledge about retrieved information may influence the performance of memory tasks. Therefore, studying the semantic proximity effect by comparing memory task performance with different levels of semantic relatedness becomes crucial. In natural language processing (NLP) studies, semantic relations can be successfully represented by learning word vectors in a large text corpus using neural networks. This study investigated the impact of semantic factors on delayed free recall tasks by creating lists that include semantically related and unrelated words obtained through pre-trained NLP models and showed how semantic proximity effect and presentation order influence recall performance. The fastText and Word2Vec models were used to obtain Turkish word embeddings, allowing for the organization of words according to their semantic relatedness. Human raters then validated the word lists. The effect of semantic relatedness on recall dynamics was later compared across four different conditions of list relatedness and embedding models used to create the lists (fastText-related, fastText-unrelated, Word2Vec-related, Word2Vec-unrelated). Our results showed a significant positive correlation between cosine similarity values and human judgment, later indicating how semantic proximity effect and presentation order influenced the recall probability and retrieval dynamics. Different levels of semantic relatedness and choice of word embeddings played a role in the likelihood of recall. Therefore, this study suggests that word embeddings obtained from neural networks can represent and manipulate semantic relations in memory studies and that semantic proximity effect and presentation order influence different levels of semantic relatedness recall dynamics.</p>