# Large Language Model-Powered Automated Assessment: A Systematic Review
## 5 Conclusions  
This study demonstrates that LLMPAA can significantly reduce educator workload and accelerate assessment processes. Their ability to analyze large volumes of text in a short period enables real-time monitoring of student progress and facilitates fast, personalized feedback, offering a major advantage for adaptive learning environments [18; 20; 39; 88]. However, claims regarding cost reduction must be evaluated cautiously, as deploying and fine-tuning these systems requires substantial investments in computing infrastructure and technical expertise. Despite these benefits, several critical limitations persist. Most notably, algorithmic biases continue to threaten fair assessment, which can disadvantage students from diverse linguistic and cultural backgrounds [10]. Issues such as performance inconsistency, limited explainability, and hallucinated or incorrect feedback weaken the reliability of LLMPAA, particularly in high-stakes or creative tasks. Furthermore, the predominance of English-language datasets in existing research limits the generalizability of these systems across multilingual and culturally varied contexts. Unresolved issues include the impact of LLMPAA on student motivation, creativity, and perceptions of fairness in evaluation--areas that remain underexplored in empirical research [59]. In addition, privacy and data security concerns persist, especially when commercial LLMs process student data on external servers. Future research should address these limitations through cross-cultural validation studies, investigate the pedagogical and emotional effects of automated feedback, and establish robust governance models for ethical implementation [11]. In addition to these challenges, the rapidly evolving landscape of LLMs presents a temporal limitation for current findings. Since the data collection phase of this review, newer and more advanced models--such as Grob 3 and DeepSeek-V3--have emerged. Grob 3, developed by xAI, was trained on the Colossus supercluster with ten times the computation of previous models, demonstrating significant improvements in reasoning, mathematics, coding, and instruction-following tasks [89]. DeepSeek-V3, on the other hand, is a mixture-of-experts model with 671 billion total parameters, utilizing innovative architectural features like multi-head latent attention and a multi-token prediction training objective, leading to enhanced performance in multilingual tasks, particularly in languages such as Chinese [90]. The emergence of such models suggests that some of the models analyzed in this review may already be outpaced. Therefore, continuous re-evaluation and benchmarking against the most current LLMs are essential to maintain the validity and applicability of LLMPAA. Future research should integrate these recent models and assess their effectiveness across a broader range of linguistic and cultural contexts to ensure sustained relevance and fairness. The adoption of hybrid approaches--such as RLHF--and the development of interpretable model designs may offer promising pathways to preserve the scalability of LLMPAA while mitigating associated risks.  
This research received no external funding.  
Not applicable.  
The author declares no conflicts of interest.  
The following abbreviations are used in this manuscript:  
AES Automated essay scoring  
ASAG Automated short answer grading  
BERT Bidirectional encoder representations from transformers  
ICC Intraclass correlation coefficient  
LLMPAA Large Language Model-Powered Automated Assessment  
LWK Linear weighted kappa  
ML Machine learning  
NLP Natural language processing  
PRISMA Preferred Reporting Items for Systematic Reviews and Meta-Analyses  
RLHF Reinforcement learning with human feedback  
QWK Quadratic weighted kappa